Deep Learning-Based Image and Video Compression: A List of Recent Publications
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This is a list of recent publications regarding deep learning-based image and video compression. This list is maintained by the Future Video Coding team at the University of Science and Technology of China (USTC-FVC). Last updated on September 16, 2022 by Mr. Yanchen Zuo and Ms. Hang Chen.
Note that this list only includes newer publications. Older publications have been included in some overview papers listed below, and do not appear in the following.
If you feel that a publication shall be included into this list, please kindly write to Prof. Dong Liu (dongeliu AT ustc DOT edu DOT cn). We are grateful to that.
Most of the listed publications come from top-tier journals and prestigious conferences. These journals and conferences, as well as the numbers of publications included in this list, as summarized below.
Journals:
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 51
IEEE Transactions on Image Processing (TIP), 41
IEEE Transactions on Multimedia (TMM), 23
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 7
Journal of Visual Communication and Image Representation (JVCIR), 8
Conferences:
AAAI Conference on Artificial Intelligence (AAAI), 2
Data Compression Conference (DCC), 79
European Conference on Computer Vision (ECCV), 9
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 31
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), 79
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 28
International Conference on Computer Vision (ICCV), 9
International Conference on Image Processing (ICIP), 63
International Conference on Multimedia and Expo (ICME), 21
International Conference on Visual Communication and Image Processing (VCIP), 47
International Symposium on Circuits and Systems (ISCAS), 37
Neural Information Processing Systems (NeurIPS), 5
Picture Coding Symposium (PCS), 46
Overview
Ma S, Zhang X, Jia C, et al. Image and video compression with neural networks: A review. TCSVT 2019 [DOI]
Zhang Y, Zhang C, Fan R, et al. Recent Advances on HEVC Inter-frame Coding: From Optimization to Implementation and Beyond. TCSVT 2019 [DOI]
Liu D, Li Y, Lin J, et al. Deep learning-based video coding: A review and a case study. ACM CSUR 2020 [DOI]
Ding D, Ma Z, Chen D, et al. Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies. Proc. IEEE (accepted) [arxiv]
Deep Schemes
Image Coding
N Ahuja, P Datta, B Kanzariya, VS Somayazulu, O Tickoo. Neural Rate Estimator and Unsupervised Learning for Efficient Distributed Image Analytics in Split-DNN models. CVPR 2023 [DOI]
R Feng, Z Guo, W Li, Z Chen. NVTC: Nonlinear Vector Transform Coding. CVPR 2023 [DOI]
X Zhang, X Wu. LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression. CVPR 2023
Y Yu, Y Wang, W Yang, S Lu, YP Tan, AC Kot. Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger. CVPR 2023 [DOI]
C Qi, X Yang, KL Cheng, YC Chen, Q Chen. Real-time 6K Image Rescaling with Rate-distortion Optimization. CVPR 2023 [DOI]
S Jeon, KP Choi, Y Park, CS Kim. Context-Based Trit-Plane Coding for Progressive Image Compression. CVPR 2023 [DOI]
J Liu, H Sun, J Katto. Learned Image Compression with Mixed Transformer-CNN Architectures. CVPR 2023 [DOI]
J Liu, H Sun, J Katto. Computationally-Efficient Neural Image Compression with Shallow Decoders. ICCV 2023 [url]
J Park, J Lee, M Kim. COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability. ICCV 2023 [url]
S Shen, H Yue, J Yang. Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression. ICCV 2023 [url]
M Li, L Shen, P Ye, G Feng, Z Wang. RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionary. ICCV 2023 [url]
Yi-Hsin Chen, Ying-Chieh Weng, Chia-Hao Kao, Cheng Chien, Wei-Chen Chiu, Wen-Hsiao Peng. TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception. ICCV 2023 [url]
L Tao, W Gao, G Li, C Zhang. AdaNIC: Towards Practical Neural Image Compression via Dynamic Transform Routing. ICCV 2023 [url]
R Feng, Y Gao, X Jin, R Feng, Z Chen. Semantically Structured Image Compression via Irregular Group-Based Decoupling. ICCV 2023 [url]
KL Cheng, Y Xie, Q Chen. Optimizing Image Compression via Joint Learning with Denoising. ECCV 2022 [DOI]
M Li, S Gao, Y Feng, Y Shi, J Wang. Content-Oriented Learned Image Compression. ECCV 2022 [DOI]
T Xu, Y Wang, D He, C Gao, H Gao, K Liu, H Qin. Multi-Sample Training for Neural Image Compression. NeurIPS 2022 [url]
C Gao, T Xu, D He, Y Wang, H Qin. Flexible Neural Image Compression via Code Editing. NeurIPS 2022 [url]
J Lee, S Jeong, M Kim. Selective compression learning of latent representations for variable-rate image compression. NeurIPS 2022 [url]
Y Bai, X Yang, X Liu, J Jiang, Y Wang, X Ji, W Gao. Towards End-to-End Image Compression and Analysis with Transformers. AAAI 2022 [url]
F Chen, Y Xu, L Wang. Two-Stage Octave Residual Network for End-to-End Image Compression. AAAI 2022 [url]
L Cao, A Jiang, W Li, H Wu, N Ye. OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression. AAAI 2022 [url]
Y Huang, B Chen, S Qin, J Li, Y Wang, T Dai, ST Xia. Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain. AAAI 2023 [url]
Y Qian, M Lin, X Sun, Z Tan, R Jin. Entroformer: A Transformer-based Entropy Model for Learned Image Compression. ICLR 2022 [url]
Y Zhu, Y Yang, T Cohen. Transformer-based Transform Coding. ICLR 2022 [url]
H Liu, G Zhang, J Chen, AJ Khisti. Lossy compression with distribution shift as entropy constrained optimal transport. ICLR 2022 [url]
A Liu, S Mandt, GV Broeck. Lossless Compression with Probabilistic Circuits. ICLR 2022 [url]
X Zhang, J Shao, J Zhang. LDMIC: Learning-based Distributed Multi-view Image Coding. ICLR 2023 [url]
MJ Muckley, A El-Nouby, K Ullrich, H Jégou, J Verbeek. Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. ICML 2023 [url]
Rogier Van der Sluijs, Maya Varma, Jip Prince, Curtis Langlotz, Akshay S Chaudhari. Diagnostically Lossless Compression of Medical Images. ICML Workshop 2023 [url]
Berivan Isik, Yibo Yang, Daniel Severo, Karen Ullrich, Robert Bamler, Stephan Mandt. Neural Compression: From Information Theory to Applications. ICML Workshop 2023 [url]
Yang Sui, Zhuohang Li, Ding Ding, Xiang Pan, Xiaozhong Xu, Shan Liu, Zhenzhong Chen. Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations. ICML Workshop 2023 [url]
Ruihan Yang, Stephan Mandt. Lossy Image Compression with Conditional Diffusion Model. ICML Workshop 2023 [url]
Tuan Pham, Yibo Yang, Stephan Mandt. Autoencoding Implicit Neural Representations for Image Compression. ICML Workshop 2023 [url]
Berivan Isik, Yibo Yang, Daniel Severo, Karen Ullrich, Robert Bamler, Stephan Mandt, Stephan Mandt. Neural Compression: From Information Theory to Applications. ICML Workshop 2023[url]
Eric Lei, Yigit Berkay Uslu, Hamed Hassani, Shirin Bidokhti. Text + Sketch: Image Compression at Ultra Low Rates. ICML Workshop 2023 [url]
Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura. Neural Image Compression: Generalization, Robustness, and Spectral Biases. ICML Workshop 2023 [url]
Wei Luo, Bo Chen. Neural Image Compression with Quantization Rectifier. ICML Workshop 2023 [url]
Hiroaki Akutsu, Ko Arai. Fast Autoregressive Bit Sequence Modeling for Lossless Compression. ICML Workshop 2023 [url]
Wei Jiang, Ronggang Wang. MLIC++++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression. ICML Workshop 2023 [url]
Z Yan, F Wen, P Liu. Optimally Controllable Perceptual Lossy Compression. ICML 2022 [url]
Z Guo, Z Zhang, R Feng, Z Chen. Soft then Hard: Rethinking the Quantization in Neural Image Compression. ICML 2021 [url]
Paper list: Challenge on Learned Image Compression. CVPR Workshops 2022 19 papers. [url]
Zhao L, Bai H, Wang A, et al. Multiple description convolutional neural networks for image compression. TCSVT 2018 [DOI]
Schiopu I, Munteanu A. Deep-learning based lossless image coding. TCSVT 2019 [DOI]
D. Mishra, S. K. Singh and R. K. Singh. Wavelet-based Deep Auto Encoder-Decoder (WDAED) based Image Compression. TCSVT 2020 [DOI]
Y. Wang, D. Liu, S. Ma, F. Wu and W. Gao. Ensemble Learning-Based Rate-Distortion Optimization for End-to-End Image Compression. TCSVT 2020 [DOI]
Z. Guo, Z. Zhang, R. Feng and Z. Chen. Causal Contextual Prediction for Learned Image Compression. TCSVT 2022 [DOI]
Y. Wu, X. Li, Z. Zhang, X. Jin and Z. Chen. Learned Block-Based Hybrid Image Compression. TCSVT 2022 [DOI]
Cai C, Chen L, Zhang X, et al. End-to-end optimized ROI image compression. TIP 2019 [DOI]
Cai J, Cao Z, Zhang L. Learning a single tucker decomposition network for lossy image compression with multiple bits-per-pixel rates. TIP 2020 [DOI]
Chen T, Liu H, Ma Z, et al. End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling. TIP 2021 [DOI]
U. Akpinar, E. Sahin, M. Meem, R. Menon and A. Gotchev. Learning Wavefront Coding for Extended Depth of Field Imaging. TIP 2021 [DOI]
J. Wang, Y. Duan, X. Tao, M. Xu and J. Lu. Semantic Perceptual Image Compression With a Laplacian Pyramid of Convolutional Networks. TIP 2021 [DOI]
T. Dardouri, M. Kaaniche, A. Benazza-Benyahia and J. -C. Pesquet. Dynamic Neural Network for Lossy-to-Lossless Image Coding. TIP 2022 [DOI]
Y. -H. Chao, H. Hong, G. Cheung and A. Ortega. Pre-Demosaic Graph-Based Light Field Image Compression. TIP 2022 [DOI]
Z. Chen, S. Gu, G. Lu and D. Xu. Exploiting Intra-Slice and Inter-Slice Redundancy for Learning-Based Lossless Volumetric Image Compression. TIP 2022 [DOI]
N. Mahmoudian Bidgoli, R. G. de A. Azevedo, T. Maugey, A. Roumy and P. Frossard. OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression. TIP 2022 [DOI]
S. Duan, H. Chen and J. Gu. JPD-SE: High-Level Semantics for Joint Perception-Distortion Enhancement in Image Compression. TIP 2022 [DOI]
H. Son, T. Kim, H. Lee and S. Lee. Enhanced Standard Compatible Image Compression Framework Based on Auxiliary Codec Networks. TIP 2022 [DOI]
Cheng Z, Sun H, Takeuchi M, et al. Energy compaction-based image compression using convolutional autoencoder. TMM 2019 [DOI]
Ma H, Liu D, Xiong R, et al. iWave: CNN-Based Wavelet-Like Transform for Image Compression. TMM 2019 [DOI]
Yin W, Shi Y, Zuo W, et al. A Co-Prediction based Compression Scheme for Correlated Images. TMM 2019 [DOI]
Y. Chen, B. Tan, J. Wu, Z. Zhang and H. Ren. A Deep Image Coding Scheme With Generative Network to Learn From Correlated Images. TMM 2021 [DOI]
M. Akbari, J. Liang, J. Han and C. Tu. Learned Multi-Resolution Variable-Rate Image Compression With Octave-Based Residual Blocks. TMM 2021 [DOI]
Y. Mei, L. Li, Z. Li and F. Li. Learning-Based Scalable Image Compression With Latent-Feature Reuse and Prediction. TMM 2021 [DOI]
Tellez D, Litjens G, van der Laak J, et al. Neural image compression for gigapixel histopathology image analysis. TPAMI 2019 [DOI]
Ma H, Liu D, Yan N, et al. End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform. TPAMI 2020 [DOI]
M. Li, W. Zuo, S. Gu, J. You and D. Zhang. Learning Content-Weighted Deep Image Compression. TPAMI 2020 [DOI]
H. Ma, D. Liu, N. Yan, H. Li and F. Wu. End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform. TPAMI 2022 [DOI]
Y. Hu, W. Yang, Z. Ma and J. Liu, "Learning End-to-End Lossy Image Compression: A Benchmark. TPAMI 2022 [DOI]
Jia X, Wei X, Cao X, et al. Comdefend: An efficient image compression model to defend adversarial examples. CVPR 2019 [DOI]
Mentzer F, Gool L V, Tschannen M. Learning Better Lossless Compression Using Lossy Compression. CVPR 2020 [DOI]
Lin C, Yao J, Chen F, et al. A Spatial RNN Codec for End-to-End Image Compression. CVPR 2020 [DOI]
Yoo I, Luo X, Wang Y, et al. GIFnets: Differentiable GIF Encoding Framework. CVPR 2020 [DOI]
Xi Zhang, Xiaolin Wu. Attention-Guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton. CVPR 2021 [url]
Dailan He, Yaoyan Zheng, Baocheng Sun, Yan Wang, Hongwei Qin. Checkerboard Context Model for Efficient Learned Image Compression. CVPR 2021 [url]
Ze Cui, Jing Wang, Shangyin Gao, Tiansheng Guo, Yihui Feng, Bo Bai. Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation. CVPR 2021 [url]
Xin Deng, Wenzhe Yang, Ren Yang, Mai Xu, Enpeng Liu, Qianhan Feng, Radu Timofte. Deep Homography for Efficient Stereo Image Compression. CVPR 2021 [url]
Lee J H , Jeon S , Choi K P , et al. DPICT: Deep Progressive Image Compression Using Trit-Planes. CVPR 2022 [url]
Kim J H , Heo B , Lee J S . Joint Global and Local Hierarchical Priors for Learned Image Compression CVPR 2022 [url]
Wang D , Yang W , Hu Y , et al. Neural Data-Dependent Transform for Learned Image Compression. CVPR 2022 [url]
Rhee H , Jang Y I , Kim S , et al. LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network. CVPR 2022 [url]
He D , Yang Z , Peng W , et al. ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding. CVPR 2022 [url]
Lu, Guo and Zhong, Tianxiong and Geng, Jing and Hu, Qiang and Xu, Dong. Learning Based Multi-Modality Image and Video Compression CVPR 2022 [url]
Lei, Jianjun and Liu, Xiangrui and Peng, Bo and Jin, Dengchao and Li, Wanqing and Gu, Jingxiao. Deep Stereo Image Compression via Bi-Directional Coding. CVPR 2022 [url]
Zhu X , Song J , Gao L , et al. Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression. CVPR 2022 [url]
Kang, Ning and Qiu, Shanzhao and Zhang, Shifeng and Li, Zhenguo and Xia, Shu-Tao. PILC: Practical Image Lossless Compression With an End-to-End GPU Oriented Neural Framework. CVPR 2022 [url]
Zou, Renjie and Song, Chunfeng and Zhang, Zhaoxiang. The Devil Is in the Details: Window-Based Attention for Image Compression. CVPR 2022 [url]
Guo, Lina and Shi, Xinjie and He, Dailan and Wang, Yuanyuan and Ma, Rui and Qin, Hongwei and Wang, Yan. Practical Learned Lossless JPEG Recompression With Multi-Level Cross-Channel Entropy Model in the DCT Domain. CVPR 2022 [url]
Akutsu H, Naruko T. End-to-End Learned ROI Image Compression. CVPR Workshops 2019 [url]
Cheng Z, Sun H, Takeuchi M, et al. Deep Residual Learning for Image Compression. CVPR Workshops 2019 [url]
Wang T. Joint learned and traditional image compression for transparent coding. CVPR Workshops 2019 [url]
Akyazi P, Ebrahimi T. Learning-based image compression using convolutional autoencoder and wavelet decomposition. CVPR Workshops 2019 [url]
Lee W C, Alexandre D, Chang C P, et al. Learned Image Compression with Residual Coding. CVPR Workshops 2019 [url]
Liu H, Chen T, Shen Q, et al. Practical Stacked Non-local Attention Modules for Image Compression. CVPR Workshops 2019 [url]
Chang C P, Alexandre D, Peng W H, et al. Description of Challenge Proposal by NCTU: An Autoencoder-based Image Compressor with Principle Component Analysis and Soft-Bit Rate Estimation. CVPR Workshops 2019 [url]
Wen S, Zhou J, Nakagawa A, et al. Variational Autoencoder based Image Compression with Pyramidal Features and Context Entropy Model. CVPR Workshops 2019 [url]
Zhou L, Sun Z, Wu X, et al. End-to-end Optimized Image Compression with Attention Mechanism. CVPR Workshops 2019 [url]
Guo Z, Wu Y, Feng R, et al. 3-D Context Entropy Model for Improved Practical Image Compression. CVPR Workshops 2020 [url]
Feng R, Wu Y, Guo Z, et al. Learned Video Compression with Feature-level Residuals. CVPR Workshops 2020 [url]
Xu J, Lytchier A, Cursio C, et al. Efficient Context-Aware Lossy Image Compression. CVPR Workshops 2020 [url]
Lee W C, Hang H M. A Hybrid Image Codec With Learned Residual Coding. CVPR Workshops 2020 [url]
Yang J, Yang C, Ma Y, et al. Learned Low Bit-Rate Image Compression With Adversarial Mechanism. CVPR Workshops 2020 [url]
Sun H, Liu C, Katto J, et al. An Image Compression Framework With Learning-Based Filter. CVPR Workshops 2020 [url]
Gao W, Tao L, Zhou L, et al. Low-Rate Image Compression With Super-Resolution Learning. CVPR Workshops 2020 [url]
Zhou J, Nakagawa A, Kato K, et al. Variable Rate Image Compression Method with Dead-zone Quantizer. CVPR Workshops 2020 [url]
van Rozendaal T, Sautiere G, Cohen T S. Lossy Compression with Distortion Constrained Optimization. CVPR Workshops 2020 [url]
Brand, Fabian and Fischer, Kristian and Kaup, Andre. Rate-Distortion Optimized Learning-Based Image Compression Using an Adaptive Hierachical Autoencoder With Conditional Hyperprior. CVPR Workshops 2021 [url]
Cheng, Zhengxue and Fu, Ting and Hu, Jiapeng and Guo, Li and Wang, Shihao and Zhao, Xiongxin and Zhou, Dajiang and Song, Yang. Perceptual Image Compression Using Relativistic Average Least Squares GANs. CVPR Workshops 2021 [url]
Yung-Han Ho, Chih-Chun Chan, Wen-Hsiao Peng, Hsueh-Ming Hang. End-to-End Learned Image Compression With Augmented Normalizing Flows. CVPR Workshops 2021 [url]
Khawar Islam, L. Minh Dang, Sujin Lee, Hyeonjoon Moon. Image Compression With Recurrent Neural Network and Generalized Divisive Normalization. CVPR Workshops 2021 [url]
Yuyang Wu, Zhiyang Qi, Huiming Zheng, Lvfang Tao, Wei Gao. Deep Image Compression With Latent Optimization and Piece-Wise Quantization Approximation. CVPR Workshops 2021 [url]
Akifumi Suzuki, Hiroaki Akutsu, Takahiro Naruko, Koki Tsubota, Kiyoharu Aizawa. Learned Image Compression With Super-Resolution Residual Modules and DISTS Optimization. CVPR Workshops 2021 [url]
Liu J, Wang S, Urtasun R. DSIC: Deep Stereo Image Compression. ICCV 2019 [DOI]
Zhou X, Xu L, Liu S, et al. An Efficient Compressive Convolutional Network for Unified Object Detection and Image Compression. AAAI 2019 [DOI]
Townsend J, Bird T, Kunze J, et al. HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models. ICLR 2020 [url]
Cheng Z, Sun H, Takeuchi M, et al. Learned Lossless Image Compression with A Hyperprior and Discretized Gaussian Mixture Likelihoods. ICASSP 2020 [DOI]
Kim J H, Choi J H, Chang J, et al. Efficient Deep Learning-Based Lossy Image Compression Via Asymmetric Autoencoder and Pruning. ICASSP 2020 [DOI]
D. Lopes, J. Ascenso, C. Brites and F. Pereira. Image Coding with Neural Network-Based Colorization. ICASSP 2021 [DOI]
D. T. Nguyen, M. Quach, G. Valenzise and P. Duhamel. Learning-Based Lossless Compression of 3D Point Cloud Geometry. ICASSP 2021 [DOI]
M. Ding, J. Li, M. Ma and X. Fan. SNR-Adaptive Deep Joint Source-Channel Coding for Wireless Image Transmission. ICASSP 2021 [DOI]
Z. Zhao, C. Jia, S. Wang, S. Ma and J. Yang. Learned Image Compression Using Adaptive Block-Wise Encoding and Reconstruction Network. ISCAS 2021 [DOI]
Z. Guo, J. Fu, R. Feng and Z. Chen. Accelerate Neural Image Compression with Channel-Adaptive Arithmetic Coding. ISCAS 2021 [DOI]
Li S, Zheng Z, Dai W, et al. Lossy image compression with filter bank based convolutional networks. DCC 2019 [DOI]
Diao E, Ding J, Tarokh V. DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. DCC 2020 [DOI]
Mali A, Ororbia A G, Giles C L. The Sibling Neural Estimator: Improving Iterative Image Decoding with Gradient Communication. DCC 2020 [DOI]
Luo J, Li S, Dai W, et al. Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding. DCC 2020 [DOI]
Ahanonu E, Marcellin M, Bilgin A. Lossless Multi-component Image Compression Based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks. DCC 2020 [DOI]
Xu Y, Zhang J. Invertible Resampling-Based Layered Image Compression. DCC 2021 [DOI]
Ye Z, Li Z, Huang X, et al. Joint Asymmetric Convolution Block and Local/Global Context Optimization for Learned Image Compression. DCC 2021 [DOI]
Akbari M, Liang J, Han J, et al. Learned Variable-Rate Image Compression With Residual Divisive Normalization. ICME 2019 [DOI]
Chang J, Mao Q, Zhao Z, et al. Layered conceptual image compression via deep semantic synthesis. ICIP 2019 [DOI]
Alexandre D, Chang C P, Peng W H, et al. Learned image compression with soft bit-based rate-distortion optimization. ICIP 2019 [DOI]
Mao Q, Wang S, Zhang X, et al. Fidelity or Quality? A Region-Aware Framework for Enhanced Image Decoding via Hybrid Neural Networks. ICIP 2019 [DOI]
Rhee H, Jang Y I, Kim S, et al. Channel-Wise Progressive Learning For Lossless Image Compression. ICIP 2020 [DOI]
Su R, Cheng Z, Sun H, et al. Scalable Learned Image Compression With A Recurrent Neural Networks-Based Hyperprior. ICIP 2020 [DOI]
Schiopu I, Munteanu A. A Study Of Prediction Methods Based On Machine Learning Techniques For Lossless Image Coding. ICIP 2020 [DOI]
Lu Y, Zhu Y, Yang Y, et al. Progressive Neural Image Compression with Nested Quantization and Latent Ordering. ICIP 2021 [DOI]
Mikami Y, Tsutake C, Takahashi K, et al. An Efficient Image Compression Method Based On Neural Network: An Overfitting Approach. ICIP 2021 [DOI]
Yang C, Ma Y, Yang J, et al. Graph-Convolution Network for Image Compression. ICIP 2021 [DOI]
Yılmaz M A, Keleş O, Güven H, et al. Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression. ICIP 2021 [DOI]
Dardouri T, Kaaniche M, Benazza-Benyahia A, et al. A Neural Network Approach For Joint Optimization Of Predictors In Lifting-Based Image Coders. ICIP 2021 [DOI]
Guleryuz O G, Chou P A, Hoppe H, et al. Sandwiched Image Compression: Wrapping Neural Networks Around A Standard Codec. ICIP 2021 [DOI]
Han F, Wang J, Xiong R, et al. HDR Image Compression with Convolutional Autoencoder. VCIP 2020 [DOI]
Gao S, Zhang Y, Liu D, et al. Volumetric End-to-End Optimized Compression for Brain Images. VCIP 2020 [DOI]
Zhong T, Jin X, Tong K. 3D-CNN Autoencoder for Plenoptic Image Compression. VCIP 2020 [DOI]
D. Bhowmik, M. Elawady and K. Nogueira. Security and Forensics Exploration of Learning-based Image Coding. VCIP 2021 [DOI]
H. Zhang, F. Cricri, H. R. Tavakoli, M. Santamaria, Y. -H. Lam and M. M. Hannuksela. Learn to overfit better: finding the important parameters for learned image compression. VCIP 2021 [DOI]
X. Pan, Z. Guo and Z. Chen. Analyzing Time Complexity of Practical Learned Image Compression Models. VCIP 2021 [DOI]
E. Upenik, M. Testolina, J. Ascenso, F. Pereira and T. Ebrahimi. Large-Scale Crowdsourcing Subjective Quality Evaluation of Learning-Based Image Coding. VCIP 2021 [DOI]
Y. Xuan, C. Yang and X. Yang. Adaptive Threshold-based Sparse Representation Network for Image Compressive Sensing Reconstruction. VCIP 2021 [DOI]
Cai C, Chen L, Zhang X, et al. A novel deep progressive image compression framework. PCS 2019 [DOI]
Alex Golts, Yoav Y. Schechner. Image compression optimized for 3D reconstruction by utilizing deep neural networks. JVCIR 2021 [DOI]
Zixi Wang, Guiguang Ding, Jungong Han, Fan Li. Deep image compression with multi-stage representation. JVCIR 2021 [DOI]
Ack A , Ok B , Mkg C . MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks. JVCIR 2021 [DOI]
Daowen Li, Yingming Li, Heming Sun, Lu Yu. Deep image compression based on multi-scale deformable convolution. JVCIR 2022 [DOI]
C. Ma, Z. Wang, R. -L. Liao and Y. Ye, Improved Deep Image Compression with Joint Optimization of Cross Channel Context Model And Generalized Loop Filter. DCC 2022 [DOI]
N. Mital, E. Özyılkan, A. Garjani and D. Gündüz, Neural Distributed Image Compression Using Common Information. DCC 2022 [DOI]
J. Wang, Y. Shi, Y. Xing, N. Ling and B. Yin, Deep Correlated Image Set Compression Based on Distributed Source Coding and Multi-Scale Fusion. DCC 2022 [DOI]
M. S. Khan Gul, H. Suleman, M. Bätz and J. Keinert, RNNSC: Recurrent Neural Network-Based Stereo Compression Using Image and State Warping. DCC 2022 [DOI]
L. Liao et al, Efficient Decoder for Learned Image Compression via Structured Pruning. DCC 2022 [DOI]
S. Mijares i Verdú, J. Ballé, V. Laparra, J. B. Rapesta, M. Hernández-Cabronero and J. Serra-Sagristá, Hyperspectral remote sensing data compression with neural networks. DCC 2022 [DOI]
A. Mali, A. G. Ororbia, D. Kifer and C. L. Giles, Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder. DCC 2022 [DOI]
M. Lu, P. Guo, H. Shi, C. Cao and Z. Ma, Transformer-based Image Compression. DCC 2022 [DOI]
Y. Zhang, C. Jia, J. Chang and S. Ma, Analysis on Compressed Domain: A Multi-Task Learning Approach. DCC 2022 [DOI]
Y. Zhang, K. Lin, C. Jia and S. Ma, Interpretable Learned Image Compression: A Frequency Transform Decomposition Perspective. DCC 2022 [DOI]
M. Song, J. Choi and B. Han. Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform. ICCV 2021 [DOI]
G. Gao et al. Neural Image Compression via Attentional Multi-scale Back Projection and Frequency Decomposition. ICCV 2021 [DOI]
E. Fleig, J. Geistert, E. Bochinski, R. Jongebloed and T. Sikora. Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression. ISCAS 2023 [DOI]
Y. Zhang, G. Lu, D. Feng, C. Zhu and L. Song. Content Adaptive Checkerboard Context Model for Learned Image Compression. ISCAS 2023 [DOI]
R. Z. Heris and I. V. Bajić. Multi-Task Learning for Screen Content Image Coding. ISCAS 2023 [DOI]
Y. Pei, Y. Liu, N. Ling, Y. Ren and L. Liu. An End-to-End Deep Generative Network for Low Bitrate Image Coding. ISCAS 2023 [DOI]
D. Xue et al. iWavePro: An improved framework for iWave++. ISCAS 2023 [DOI]
H. Zhang, J. Liao, Y. Jiang, L. Li and D. Liu. Padding-Aware Learned Image Compression. ISCAS 2023 [DOI]
C. Gille, F. Guyard, M. Antonini and M. Barlaud. Learning Sparse auto-Encoders for Green AI image coding. ICASSP 2023 [DOI]
K. Fischer, F. Brand, C. Blum and A. Kaup. Saliency-Driven Hierarchical Learned Image Coding for Machines. ICASSP 2023 [DOI]
E. Özyılkan, M. Ulhaq, H. Choi and F. Racapé. Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines. DCC 2023 [DOI]
B. B. Damodaran, M. Balcilar, F. Galpin and P. Hellier. RQAT-INR: Improved Implicit Neural Image Compression. DCC 2023 [DOI]
Y. Fang et al. Fully Integerized End-to-End Learned Image Compression. DCC 2023 [DOI]
W. Duan, K. Lin, C. Jia, X. Zhang, S. Ma and W. Gao. End-to-End Image Compression via Attention-Guided Information-Preserving Module. ICME 2022 [DOI]
K. Wang, Y. Bai, D. Zhai, D. Li, J. Jiang and X. Liu. Learning Lossless Compression for High Bit-Depth Medical Imaging. ICME 2023 [DOI]
Z. Liu, H. Wang and T. Su. Learned Image Compression with Multi-Scale Spatial and Contextual Information Fusion. ICIP 2022 [DOI]
X. Fan, X. Li and Z. Chen. Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression. VCIP 2022 [DOI]
S. Zhang, L. Wang, X. Mao, F. Yang and S. Wan. Rate Controllable Learned Image Compression Based on RFL Model. VCIP 2022 [DOI]
M. Wang et al. End-to-end Image Compression with Swin-Transformer. VCIP 2022 [DOI]
H. Choi, F. Racapé, S. Hamidi-Rad, M. Ulhaq and S. Feltman. Frequency-aware Learned Image Compression for Quality Scalability. VCIP 2022 [DOI]
H. Fu, F. Liang, J. Liang, B. Li, G. Zhang and J. Han. Learned Image Compression with Inception Residual Blocks and Multi-Scale Attention Module. PCS 2022 [DOI]
P. Jia, A. B. Koyuncu, G. Gaikov, A. Karabutov, E. Alshina and A. Kaup. Learning-Based Conditional Image Coder Using Color Separation. PCS 2022 [DOI]
O. G. Guleryuz et al. Sandwiched Image Compression: Increasing the resolution and dynamic range of standard codecs. PCS 2022 [DOI]
L. Wang, X. Mao, S. Zhang and F. Yang. End-to-End Quality Controllable Image Compression. PCS 2022 [DOI]
Video Coding
L Wang, Q Hu, Q He, Z Wang, J Yu, T Tuytelaars, L Xu, M Wu. Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos. CVPR 2023 [DOI]
L Qi, J Li, B Li, H Li, Y Lu. Motion Information Propagation for Neural Video Compression. CVPR 2023 [DOI]
David Alexandre, Hsueh-Ming Hang, Wen-Hsiao Peng. Hierarchical B-Frame Video Coding Using Two-Layer CANF Without Motion Coding. CVPR 2023 [DOI]
Z Hu, D Xu. Complexity-guided Slimmable Decoder for Efficient Deep Video Compression. CVPR 2023 [DOI]
C Gomes, R Azevedo, C Schroers. Video Compression with Entropy-Constrained Neural Representations. CVPR 2023 [DOI]
Y Tian, G Lu, G Zhai, Z Gao. Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic Compression. ICCV 2023 [url]
L Tang, X Zhang, G Zhang, X Ma. Scene Matters: Model-based Deep Video Compression. ICCV 2023 [url]
Y Shi, Y Ge, J Wang, J Mao. AlphaVC: High-Performance and Efficient Learned Video Compression. ECCV 2022 [DOI]
F Mentzer, E Agustsson, J Ballé, D Minnen, N Johnston, G Toderici. Neural Video Compression Using GANs for Detail Synthesis and Propagation. ECCV 2022 [DOI]
F Mentzer, G Toderici, D Minnen, SJ Hwang, S Caelles, M Lucic, E Agustsson. VCT: A Video Compression Transformer. NeurIPS 2022 [url]
A Antsiferova, S Lavrushkin, M Smirnov, A Gushchin, D Vatolin, D Kulikov. Video compression dataset and benchmark of learning-based video-quality metrics. NeurIPS 2022 [url]
Yunfan Zhang, Ties van Rozendaal, Johann Brehmer, Markus Nagel, Taco Cohen. Implicit Neural Video Compression. ICLR 2022 [url]
JW Chang, N Sheybani, SS Hussain, M Javaheripi, S Hidano, F Koushanfar. NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression. ICLR 2023 [url]
J Xiang, K Tian, J Zhang. MIMT: Masked Image Modeling Transformer for Video Compression. ICLR 2023 [url]
T Shao, JN Shingala, A Shyam, P Yin, A Arora, S McCarthy. Low Complexity Neural Network-Based In-loop Filtering with Decomposed Split Luma-Chroma Model for Video Compression. ICML Workshop 2023 [url]
Buu Phan, Sadaf Salehkalaibar, Jun Chen, Wei Yu, Ashish Khisti. On the Choice of Perception Loss Function for Learned Video Compression. ICML Workshop 2023 [url]
G. Lu, W. Ouyang, D. Xu, X. Zhang, C. Cai and Z. Gao. DVC: An End-To-End Deep Video Compression Framework. CVPR 2019 [DOI]
Cheng Z, Sun H, Takeuchi M, et al. Learning image and video compression through spatial-temporal energy compaction. CVPR 2019 [DOI]
Lin J, Liu D, Li H, et al. M-LVC: Multiple Frames Prediction for Learned Video Compression. CVPR 2020 [DOI]
Agustsson E, Minnen D, Johnston N, et al. Scale-Space Flow for End-to-End Optimized Video Compression. CVPR 2020 [DOI]
Zhihao Hu, Guo Lu, Dong Xu. FVC: A New Framework Towards Deep Video Compression in Feature Space. CVPR 2021 [url]
Bowen Liu, Yu Chen, Shiyu Liu, Hun-Seok Kim. Deep Learning in Latent Space for Video Prediction and Compression. CVPR 2021 [url]
Lu, Guo and Zhong, Tianxiong and Geng, Jing and Hu, Qiang and Xu, Dong. Learning Based Multi-Modality Image and Video Compression CVPR 2022 [url]
Chen, Zhenghao and Lu, Guo and Hu, Zhihao and Liu, Shan and Jiang, Wei and Xu, Dong. LSVC: A Learning-Based Stereo Video Compression Framework. CVPR 2022 [url]
Hu, Zhihao and Lu, Guo and Guo, Jinyang and Liu, Shan and Jiang, Wei and Xu, Dong. Coarse-To-Fine Deep Video Coding With Hyperprior-Guided Mode Prediction. CVPR 2022 [url]
Ho M M, Zhou J, He G, et al. SR-CL-DMC: P-Frame Coding With Super-Resolution, Color Learning, and Deep Motion Compensation. CVPR Workshops 2020 [url]
Zou N, Zhang H, Cricri F, et al. End-to-End Learning for Video Frame Compression with Self-Attention. CVPR Workshops 2020 [url]
He G, Wu C, Li L, et al. A Video Compression Framework Using an Overfitted Restoration Neural Network. CVPR Workshops 2020 [url]
Wu X J, Zhang Z, Feng J, et al. End-to-end Optimized Video Compression with MV-Residual Prediction. CVPR Workshops 2020 [url]
Lombardo S, Han J, Schroers C, et al. Deep generative video compression. NeurIPS 2019 [url]
Liu J, Wang S, Ma W C, et al. Conditional Entropy Coding for Efficient Video Compression. ECCV 2020 [url]
Hu Z, Chen Z, Xu D, et al. Improving deep video compression by resolution-adaptive flow coding. ECCV 2020 [url]
Lu G, Cai C, Zhang X, et al. Content Adaptive and Error Propagation Aware Deep Video Compression. ECCV 2020 [url]
Sun W, Tang C, Li W, et al. High-quality Single-model Deep Video Compression with Frame-Conv3D and Multi-frame Differential Modulation. ECCV 2020 [url]
Chen D, Chen Q, Zhu F. Pixel-level Texture Segmentation Based AV1 Video Compression. ICASSP 2019 [DOI]
Yilmaz M A, Tekalp A M. End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression. ICIP 2020 [DOI]
Liu C, Sun H, Cheng Z, et al. Dual learning-based video coding with inception dense blocks. PCS 2019 [DOI]
Chen M, Patney A, Bovik A C. MOVI-Codec: Deep Video Compression without Motion. PCS 2021 [DOI]
G. Lu, X. Zhang, W. Ouyang, L. Chen, Z. Gao and D. Xu. An End-to-End Learning Framework for Video Compression. TPAMI 2020 [DOI]
Rippel O , Anderson A G , Tatwawadi K , et al. ELF-VC: Efficient Learned Flexible-Rate Video Coding. ICCV 2021 [url]
Li, Jiahao, Bin Li and Yan Lu. Deep Contextual Video Compression. NeurIPS 2021 [url]
G. He et al. Interlayer Restoration Deep Neural Network for Scalable High Efficiency Video Coding. TCSVT 2022 [DOI]
K. Yang, D. Liu, Z. Chen, F. Wu and W. Li. Spatiotemporal Generative Adversarial Network-Based Dynamic Texture Synthesis for Surveillance Video Coding. TCSVT 2022 [DOI]
S. Wang, C. Jia, X. Zhang, S. Wang, S. Ma and W. Gao. A Pixel-level Segmentation-Synthesis Framework for Dynamic Texture Video Compression. TCSVT 2022 [DOI]
H. Liu, M. Lu, Z. Chen, X. Cao, Z. Ma and Y. Wang. End-to-End Neural Video Coding Using a Compound Spatiotemporal Representation. TCSVT 2022 [DOI]
B. Chen, Z. Wang, B. Li, R. Lin, S. Wang and Y. Ye, Beyond Keypoint Coding: Temporal Evolution Inference with Compact Feature Representation for Talking Face Video Compression. DCC 2022 [DOI]
X. Sun, P. Liu, K. Jia and S. Chen, SAQENet: A Quality Enhancement Network for Compressed Video with Self-attention. DCC 2022 [DOI]
Z. Liu, H. Qi, Y. Han, G. Cui and Y. Zhang, A Low-complexity Neural Network for Compressed Video Post-processing in HEVC. DCC 2022 [DOI]
J. Zhou, T. Lv and X. Yi, End-to-end Distributed Video Coding. DCC 2022 [DOI]
Yang R , Yang Y , Marino J , et al. Hierarchical Autoregressive Modeling for Neural Video Compression. ICLR 2021 [url]
M. A. Yılmaz and A. M. Tekalp. End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression. TIP 2022 [DOI]
J. Lei et al. Disparity-Aware Reference Frame Generation Network for Multiview Video Coding. TIP 2022 [DOI]
M. Benjak, N. Aust, Y. Samayoa and J. Ostermann. Neural Network-based Error Concealment for B-Frames in VVC. ISCAS 2022 [DOI]
Y. Liu, S. Li, S. Zhu, S. -K. A. Yeung, X. Wen and B. Zeng. Hierarchical Coding for Talking-Head Video. ISCAS 2022 [DOI]
H. Liu, R. Yang, S. Zhu, X. Wen and B. Zeng. Luminance-Guided Chrominance Image Enhancement for HEVC Intra Coding. ISCAS 2022 [DOI]
C. Dong, H. Ma, D. Liu and J. W. Woods. Wavelet-Based Learned Scalable Video Coding. ISCAS 2022 [DOI]
R. Lin, P. Zhang, M. Wang, S. Wang and S. Kwong. Deep Video Compression for P-frame in Sub-sampled Color Spaces. ISCAS 2022 [DOI]
H. Man, C. Yu, F. Xing, Y. Cheng, B. Zheng and X. Fan. Deep Learning-Assisted Video Compression Framework. ISCAS 2022 [DOI]
Y. Zhao et al. Towards Next Generation Video Coding: from Neural Network Based Predictive Coding to In-Loop Filtering. ISCAS 2023 [DOI]
M. -J. Chen, H. -S. Xie, C. Chien, W. -H. Peng and H. -M. Hang. Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content. ISCAS 2023 [DOI]
H. Wang, N. Fu and Z. Chen. Efficient Learned Video Compression via Bidirectional Temporal Information Exploration. ISCAS 2023 [DOI]
A. Borges, M. Porto, B. Zatt and G. Correa. H.264-to-AV1 Video Transcoding Acceleration Based on Lightweight Machine Learning. ISCAS 2023 [DOI]
R. Wang, Q. Mao, S. Wang, C. Jia, R. Wang and S. Ma. Disentangled Visual Representations for Extreme Human Body Video Compression. ICME 2022 [DOI]
K. Misra, T. Ji, A. Segall and F. Bossen. Video Feature Compression for Machine Tasks. ICME 2022 [DOI]
X. Zhang, J. Shao and J. Zhang. Low-complexity Deep Video Compression with A Distributed Coding Architecture. ICME 2023 [DOI]
T. Ladune and P. Philippe. AIVC: Artificial Intelligence Based Video Codec. ICIP 2022 [DOI]
H. Jiang and L. Chen. An Efficient Content-aware Downsampling-based Video Compression Framework. VCIP 2022 [DOI]
S. M. A. K. Rajin, M. Murshed, M. Paul, S. W. Teng and J. Ma. Human pose based video compression via forward-referencing using deep learning. VCIP 2022 [DOI]
M. M. Ho, H. Sun, Z. Zhang and J. Zhou. On Pre-chewing Compression Degradation for Learned Video Compression. VCIP 2022 [DOI]
S. Pientka, M. Schäfer, J. Pfaff, H. Schwarz, D. Marpe and T. Wiegand. Deep video coding with gradient-descent optimized motion compensation and Lanczos filtering. PCS 2022 [DOI]
W. Gao et al. Optimal Tile-Based Encoding for 360-Degree Video Streaming. PCS 2022 [DOI]
P. Du, Y. Liu, N. Ling, Y. Ren and L. Liu. Generative Video Compression with a Transformer-Based Discriminator. PCS 2022 [DOI]
Perceptual or Semantic Coding
Zhu S, Liu C, Xu Z. High-definition video compression system based on perception guidance of salient information of a convolutional neural network and HEVC compression domain. TCSVT 2019 [DOI]
Sun S, He T, Chen Z. Semantic Structured Image Coding Framework for Multiple Intelligent Applications. TCSVT 2020 [DOI]
Chen L H, Bampis C G, Li Z, et al. ProxIQA: A proxy approach to perceptual optimization of learned image compression. TIP 2020 [DOI]
N. Yan, C. Gao, D. Liu, H. Li, L. Li and F. Wu. SSSIC: Semantics-to-Signal Scalable Image Coding With Learned Structural Representations. TIP 2022 [DOI]
Li M, Zuo W, Gu S, et al. Learning content-weighted deep image compression. TPAMI 2020 [DOI]
Liu Z, Xu X, Liu T, et al. Machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds. CVPR 2019 [DOI]
Aaron Chadha, Yiannis Andreopoulos. Deep Perceptual Preprocessing for Video Coding. CVPR 2021 [url]
Campos J, Meierhans S, Djelouah A, et al. Content Adaptive Optimization for Neural Image Compression. CVPR Workshops 2019 [url]
Aytekin C, Cricri F, Hallapuro A, et al. A Compression Objective and a Cycle Loss for Neural Image Compression. CVPR Workshops 2019 [url]
Akutsu H, Suzuki A, Zhong Z, et al. Ultra Low Bitrate Learned Image Compression by Selective Detail Decoding. CVPR Workshops 2020 [url]
Lee J, Kim D, Kim Y, et al. A Training Method for Image Compression Networks to Improve Perceptual Quality of Reconstructions. CVPR Workshops 2020 [url]
Man Hoang T, Zhou J, Fan Y. Image Compression With Encoder-Decoder Matched Semantic Segmentation. CVPR Workshops 2020 [url]
Veerabadran V, Pourreza R, Habibian A, et al. Adversarial Distortion for Learned Video Compression. CVPR Workshops 2020 [url]
Habibian A, Rozendaal T, Tomczak J M, et al. Video compression with rate-distortion autoencoders. ICCV 2019 [DOI]
Wang J, Deng X, Xu M, et al. Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video. ECCV 2020 [url]
Mentzer F, Toderici G, Tschannen M, et al. High-Fidelity Generative Image Compression. NeurIPS 2020 [url]
Yan N, Liu D, Li H, et al. Towards Semantically Scalable Image Coding using Semantic Map. ISCAS 2020 [DOI]
Shi J, Chen Z. Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics. ISCAS 2020 [DOI]
SWu Y, He T, Chen Z. Memorize, Then Recall: A Generative Framework for Low Bit-Rate Surveillance Video Compression. ISCAS 2020 [DOI]
Xiang C, Xu J, Yan C, et al. Generative adversarial networks based error concealment for low resolution video. ICASSP 2019 [DOI]
Garber S, Marcus R, DiLillo A, et al. Low Rate Compression of Video with Dynamic Backgrounds. DCC 2020 [DOI]
Guruvareddiar P, Prasad P. Artificial Intelligence Based Region of Interest Enhanced Video Compression. DCC 2020 [DOI]
Kim J, Lee D Y, Jeong S, et al. Perceptual Video Coding using Deep Neural Network Based JND Model. DCC 2020 [DOI]
Guo Y, Xu M, Yang L, et al. A viewport-adaptive rate control approach for omnidirectional video coding. DCC 2021 [DOI]
Chamain L D, Racapé F, Bégaint J, et al. End-to-end optimized image compression for machines, a study. DCC 2021 [DOI]
Wang S, Zhang X, Wang S, et al. Flow-Grounded Dynamic Texture Synthesis for Video Compression. DCC 2021 [DOI]
Chamain L D, Cheung S S, Ding Z. Quannet: Joint Image Compression and Classification Over Channels with Limited Bandwidth. ICME 2019 [DOI]
Ma D, Zhang F, Bull D R. Gan-Based Effective Bit Depth Adaptation for Perceptual Video Compression. ICME 2020 [DOI]
Hu Y, Yang S, Yang W, et al. Towards coding for human and machine vision: A scalable image coding approach. ICME 2020 [DOI]
Huang Z, Jia C, Wang S, et al. Visual Analysis Motivated Rate-Distortion Model for Image Coding. ICME 2021 [DOI]
Bosse S, Dietzel M, Becker S, et al. Neural Network Guided Perceptually Optimized Bit-Allocation for Block-Based Image and Video Compression. ICIP 2019 [DOI]
Wang J, Tao X, Xu M, et al. Semantic Perceptual Image Compression with a Laplacian Pyramid of Convolutional Networks. ICIP 2019 [DOI]
Cheng Z, Akyazi P, Sun H, et al. Perceptual quality study on deep learning based image compression. ICIP 2019 [DOI]
Suzuki S, Takagi M, Hayase K, et al. Image Pre-Transformation for Recognition-Aware Image Compression. ICIP 2019 [DOI]
Wang S, Wang S, Zhang X, et al. Scalable facial image compression with deep feature reconstruction. ICIP 2019 [DOI]
Patwa N, Ahuja N, Somayazulu S, et al. Semantic-Preserving Image Compression. ICIP 2020 [DOI]
Yan N, Liu D, Li H, et al. Semantically Scalable Image Coding With Compression of Feature Maps. ICIP 2020 [DOI]
Fischer K, Forsch C, Herglotz C, et al. Analysis Of Neural Image Compression Networks For Machine-To-Machine Communication. ICIP 2021 [DOI]
Yang R, Liu D, Ma S, et al. Knowledge Distillation From End-To-End Image Compression To Vvc Intra Coding For Perceptual Quality Enhancement. ICIP 2021 [DOI]
Xu Y, Zhang J. Expressive And Compressive Gan Inversion Network. ICIP 2021 [DOI]
S. Zhang, M. Mrak, L. Herranz, M. G. Blanch, S. Wan and F. Yang. DVC-P: Deep Video Compression with Perceptual Optimizations. VCIP 2021 [DOI]
G. Ren, Z. Liu, Z. Chen and S. Liu. Reinforcement Learning based ROI Bit Allocation for Gaming Video Coding in VVC. VCIP 2021 [DOI]
Kudo S, Orihashi S, Tanida R, et al. GAN-based Image Compression Using Mutual Information Maximizing Regularization. PCS 2019 [DOI]
Ye N, Pérez-Ortiz M, Mantiuk R K. Visibility Metric for Visually Lossless Image Compression. PCS 2019 [DOI]
Guo Z, Zhang Z, Chen Z. Deep scalable image compression via hierarchical feature decorrelation. PCS 2019 [DOI]
He T, Sun S, Guo Z, et al. Beyond coding: Detection-driven image compression with semantically structured bit-stream. PCS 2019 [DOI]
Zhu C, Huang Y, Xie R, et al. HEVC VMAF-oriented Perceptual Rate Distortion Optimization using CNN. PCS 2021 [DOI]
Kirmemis O, Tekalp A M. A Practical Approach for Rate-Distortion-Perception Analysis in Learned Image Compression. PCS 2021 [DOI]
Y. Hu, Y. Xu, J. Chang and J. Zhang, Semantic Neural Rendering-based Video Coding: Towards Ultra-Low Bitrate Video Conferencing. DCC 2022 [DOI]
M. Yang et al. Semantic Preprocessor for Image Compression for Machines. ICASSP 2023 [DOI]
G. Xie et al. Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation. VCIP 2022 [DOI]
S. Mohammadi and J. Ascenso. Perceptual impact of the loss function on deep-learning image coding performance. PCS 2022 [DOI]
J. Liu, H. Sun and J. Katto. Semantic Segmentation In Learned Compressed Domain. PCS 2022 [DOI]
C. Zhu, G. Lu, R. Xie and L. Song. Perceptual Video Coding Based on Semantic-Guided Texture Detection and Synthesis. PCS 2022 [DOI]
Deep Tools
Intra Prediction
Pfaff J, Schwarz H, Marpe D, et al. Video Compression Using Generalized Binary Partitioning, Trellis Coded Quantization, Perceptually Optimized Encoding, and Advanced Prediction and Transform Coding. TCSVT 2019 [DOI]
Wang Y, Fan X, Liu S, et al. Multi-scale Convolutional Neural Network Based Intra Prediction for Video Coding. TCSVT 2019 [DOI]
Schiopu I, Huang H, Munteanu A. CNN-based Intra-Prediction for Lossless HEVC. TCSVT 2019 [DOI]
Dumas T, Roumy A, Guillemot C. Context-adaptive neural network-based prediction for image compression. TIP 2019 [DOI]
Dumas T, Galpin F, Bordes P. Iterative training of neural networks for intra prediction. TIP 2020 [DOI]
T. Li, M. Xu, R. Tang, Y. Chen and Q. Xing. DeepQTMT: A Deep Learning Approach for Fast QTMT-Based CU Partition of Intra-Mode VVC. TIP2021 [DOI]
Zhu L, Kwong S, Zhang Y, et al. Generative Adversarial Network-Based Intra Prediction for Video Coding. TMM 2019 [DOI]
Sun H, Cheng Z, Takeuchi M, et al. Enhanced Intra Prediction for Video Coding by Using Multiple Neural Networks. TMM 2019 [DOI]
Lin T L, Liang K W, Huang J Y, et al. Intra Mode Prediction for H. 266/FVC Video Coding based on Convolutional Neural Network. JVCIR 2019 [DOI]
Mao J, Yu H, Gao X, et al. CNN-Based Bi-Prediction Utilizing Spatial Information for Video Coding. ISCAS 2019 [DOI]
Zhang X, Wu X. Nonlinear Prediction of Multidimensional Signals via Deep Regression with Applications to Image Coding. ICASSP 2019 [DOI]
Meyer M, Wiesner J, Schneider J, et al. Convolutional neural networks for video intra prediction using cross-component adaptation. ICASSP 2019 [DOI]
Huang H, Schiopu I, Munteanu A. Deep learning based angular intra-prediction for lossless HEVC video coding. DCC 2019 [DOI]
Helle P, Pfaff J, Schäfer M, et al. Intra picture prediction for video coding with neural networks. DCC 2019 [DOI]
Blanch M G, Blasi S, Smeaton A, et al. Chroma Intra Prediction With Attention-Based CNN Architectures. ICIP 2020 [DOI]
Jung H C, Guerin N D, Ramos R S, et al. Multi-Mode Intra Prediction for Learning-Based Image Compression. ICIP 2020 [DOI]
Meyer M, Wiesner J, Rohlfing C. Optimized Convolutional Neural Networks for Video Intra Prediction. ICIP 2020 [DOI]
Brand F, Seiler J, Kaup A. Introducing Latent Space Correlation to Conditional Autoencoders for Intra Prediction. VCIP 2020 [DOI]
Brand F, Seiler J, Kaup A. Intra To Inter: Towards Intra Prediction for Learning-Based Video Coders Using Optical Flow. ICIP 2021 [DOI]
Sun H, Yu L, Katto J. Fully Neural Network Mode Based Intra Prediction of Variable Block Size. VCIP 2020 [DOI]
M. Saldanha, G. Sanchez, C. Marcon and L. Agostini. Learning-Based Complexity Reduction Scheme for VVC Intra-Frame Prediction. VCIP 2021 [DOI]
Brand F, Seiler J, Kaup A. Intra Frame Prediction for Video Coding Using a Conditional Autoencoder Approach. PCS 2019 [DOI]
Laude T, Ostermann J. Contour-based Intra Coding Using Gaussian Processes and Neural Networks. PCS 2021 [DOI]
D. Roy, T. Guha and V. Sanchez, Graph-based Transform based on 3D Convolutional Neural Network for Intra-Prediction of Imaging Data. DCC 2022 [DOI]
T. Tang, C. You, Z. Li, R. Zhang and H. Zou. Ultra-Lightweight CNN Based Fast Intra Prediction for VVC Screen Content Coding. ISCAS 2023 [DOI]
L. Xu, Y. Yu, H. Yu and D. Wang. Autoencoder-based intra prediction with auxiliary feature. VCIP 2022 [DOI]
Y. -H. Ho, C. -H. Kao, W. -H. Peng and P. -C. Hsieh. Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265. VCIP 2022 [DOI]
H. Zeng, Y. Huang, T. Zhao, L. Wu, W. Feng and G. Cai. Effective VVC Intra Prediction Based on Ensemble Learning. PCS 2022 [DOI]
Inter Prediction
X Zhang, S Yang, W Luo, L Gao, W Zhang. Video Compression Artifact Reduction by Fusing Motion Compensation and Global Context in a Swin-CNN Based Parallel Architecture. AAAI 2023 [url]
Choi G, Heo P G, Park H W. Triple-Frame-Based Bi-Directional Motion Estimation for Motion-Compensated Frame Interpolation. TCSVT 2018 [DOI]
Zhang Y, Chen L, Yan C, et al. Weighted Convolutional Motion-Compensated Frame Rate Up-Conversion Using Deep Residual Network. TCSVT 2018 [DOI]
Choi H, Bajić I V. Deep frame prediction for video coding. TCSVT 2019 [DOI]
Mao J, Yu L. Convolutional Neural Network Based Bi-prediction Utilizing Spatial and Temporal Information in Video Coding. TCSVT 2019 [DOI]
X. Meng, X. Deng, S. Zhu, X. Zhang and B. Zeng. A Robust Quality Enhancement Method Based on Joint Spatial-Temporal Priors for Video Coding. TCSVT 2020 [DOI]
H. Zhang, L. Song, L. Li, Z. Li and X. Yang. Compression Priors Assisted Convolutional Neural Network for Fractional Interpolation. TCSVT 2020 [DOI]
X. Cheng and Z. Chen. A Multi-Scale Position Feature Transform Network for Video Frame Interpolation. TCSVT 2019 [DOI]
S. Huo, D. Liu, B. Li, S. Ma, F. Wu and W. Gao. Deep Network-Based Frame Extrapolation With Reference Frame Alignment. TCSVT 2020 [DOI]
Zhang H, Song L, Li L, et al. Compression Priors Assisted Convolutional Neural Network for Fractional Interpolation. TCSVT 2020 [DOI]
Yu L, Shen L, Yang H, et al. A Distortion-Aware Multi-task Learning Framework for Fractional Interpolation in Video Coding. TCSVT 2020 [DOI]
H. Liu et al. Neural Video Coding Using Multiscale Motion Compensation and Spatiotemporal Context Model. TCSVT 2020 [DOI]
Y. Wang, X. Fan, R. Xiong, D. Zhao and W. Gao. Neural Network-Based Enhancement to Inter Prediction for Video Coding. TCSVT 2022 [DOI]
D. Jin, J. Lei, B. Peng, W. Li, N. Ling and Q. Huang. Deep Affine Motion Compensation Network for Inter Prediction in VVC. TCSVT 2022 [DOI]
Wang Z, Wang S, Zhang X, et al. Three-Zone Segmentation-Based Motion Compensation for Video Compression. TIP 2019 [DOI]
Li B, Han J, Xu Y, et al. Optical Flow Based Co-Located Reference Frame for Video Compression. TIP 2020 [DOI]
D. Ding, X. Gao, C. Tang and Z. Ma. Neural Reference Synthesis for Inter Frame Coding. TIP 2022 [DOI]
Liu J, Xia S, Yang W. Deep reference generation with multi-domain hierarchical constraints for inter prediction. TMM 2019 [DOI]
Castro da Silva R, Guerin D, Sanches P, et al. Joint Motion and Residual Information Latent Representation for P-Frame Coding. CVPR Workshops 2020 [url]
Ho Y H, Chan C C, Alexandre D, et al. P-Frame Coding Proposal by NCTU: Parametric Video Prediction Through Backprop-Based Motion Estimation. CVPR Workshops 2020 [url]
Xia S, Yang W, Hu Y, et al. Switch mode based deep fractional interpolation in video coding. ISCAS 2019 [DOI]
Hu Y, Xia S, Yang W, et al. Memory-Augmented Auto-Regressive Network for Frame Recurrent Inter Prediction. ISCAS 2020 [DOI]
Bégaint J, Galpin F, Guillotel P, et al. Deep frame interpolation for video compression. DCC 2019 [DOI]
Tao H, Qian J, Yu L, et al. Bi-Prediction Enhancement with Deep Frame Prediction Network for Versatile Video Coding. DCC 2021 [DOI]
Zhang Q, Huang X, Yin H, et al. Deformable Convolution Network based Invertibility-Driven Interpolation Filter for HEVC. DCC 2021 [DOI]
Zhang H, Li L, Song L, et al. Advanced cnn based motion compensation fractional interpolation. ICIP 2019 [DOI]
Murn L, Blasi S, Smeaton A F, et al. Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding. ICIP 2020 [DOI]
Lei J, Zhang Z, Liu D, et al. Deep Virtual Reference Frame Generation For Multiview Video Coding. ICIP 2020 [DOI]
Alexandre D, Hang H M, Peng W H, et al. Deep Video Compression for Interframe Coding. ICIP 2021 [DOI]
Guo Y, Liu Z, Chen Z, et al. Deep Inter Coding with Interpolated Reference Frame for Hierarchical Coding Structure. VCIP 2020 [DOI]
Z. Zhu, L. Zhao, X. Lin, X. Guo and J. Chen. Deep Inter Prediction via Reference Frame Interpolation for Blurry Video Coding. VCIP 2021 [DOI]
Laude T, Haub F, Ostermann J. HEVC Inter Coding using Deep Recurrent Neural Networks and Artificial Reference Pictures. PCS 2019 [DOI]
Tao H, Yu L, Kuang Z, et al. An Extended Skip Strategy for Inter Prediction. PCS 2019 [DOI]
Brand F, Seiler J, Kaup A. Switchable Motion Models for Non-Block-Based Inter Prediction in Learning-Based Video Coding. PCS 2021 [DOI]
R. Pourreza and T. Cohen. Extending Neural P-frame Codecs for B-frame Coding. ICCV 2021 [DOI]
K. Q. Dinh and K. Pyo Choi. Learned Video Coding with Motion Compensation Mixture Model. ICASSP 2023 [DOI]
F. Wang, H. Ruan, F. Xiong, J. Yang, L. Li and R. Wang. Butterfly: Multiple Reference Frames Feature Propagation Mechanism for Neural Video Compression. DCC 2023 [DOI]
J. Jia, Z. Liu, X. Xu, S. Liu and Z. Chen. Deep Reference Frame Interpolation based Inter Prediction Enhancement for Versatile Video Coding. VCIP 2022 [DOI]
Cross-Channel Prediction
Zhu L, Zhang Y, Wang S, et al. Deep Learning-Based Chroma Prediction for Intra Versatile Video Coding. TCSVT 2020 [DOI]
R. Huang et al, Video Compression via Inter-frame Chroma Prediction. DCC 2022 [DOI]
Transform & Quantization
Kwong S, Zhou M, Xuekai W E I, et al. Rate Control Method Based on Deep Reinforcement Learning for Dynamic Video Sequences in HEVC. TMM 2020 [DOI]
Choi J, Han B. Task-Aware Quantization Network for JPEG Image Compression. ECCV 2020 [url]
Wang H, Yu S, Zhang Y, et al. Hard-Decision Quantization Algorithm Based on Deep Learning in Intra Video Coding. DCC 2019 [DOI]
Li B, Akbari M, Liang J, et al. Deep Learning-based Image Compression with Trellis Coded Quantization. DCC 2020 [DOI]
Huang C H, Wu J L. JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures. DCC 2021 [DOI]
Puchala D, Stokfiszewski K. Convolutional Neural Network for Image Compression with Application to JPEG Standard. DCC 2021 [DOI]
Roy D, Guha T, Sanchez V. Graph Based Transforms based on Graph Neural Networks for Predictive Transform Coding. DCC 2021 [DOI]
Tsubota K, Aizawa K. Comprehensive Comparisons Of Uniform Quantizers For Deep Image Compression. ICIP 2021 [DOI]
Dimitriadis A, Taubman D. Augmenting JPEG2000 With Wavelet Coefficient Prediction. ICIP 2020 [DOI]
Li X, Naman A, Taubman D. Machine-Learning Based Secondary Transform for Improved Image Compression in JPEG2000. ICIP 2021 [DOI]
Dumas T, Galpin F, Bordes P. Combined neural network-based intra prediction and transform selection. PCS 2021 [DOI]
Yang K, Liu D, Wu F. Deep Learning-Based Nonlinear Transform for HEVC Intra Coding. VCIP 2020 [DOI]
Ouyang T, Chen Z, Liu S. Towards Quantized DCT Coefficients Restoration for Compressed Images. VCIP 2020 [DOI]
K. Misra, A. Segall and B. Choi. Multiscale convolutional neural networks for in-loop video restoration. DCC 2023 [DOI]
K. Fischer, F. Brand, C. Herglotz and A. Kaup. Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-Driven Image Coding. ICIP 2022 [DOI]
Entropy Coding
Li M, Ma K, You J, et al. Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression. TIP 2020 [DOI]
Zhou J, Wen S, Nakagawa A, et al. Multi-scale and context-adaptive entropy model for image compression. CVPR Workshops 2019 [url]
Lee J, Cho S, Jeong S, et al. Extended End-to-End optimized Image Compression Method based on a Context-Adaptive Entropy Model. CVPR Workshops 2019 [url]
Flamich G, Havasi M, Hernández-Lobato J M. Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. NeurIPS 2020 [url]
Ladune T, Philippe P, Hamidouche W, et al. Binary Probability Model for Learning Based Image Compression. ICASSP 2020 [DOI]
Ma C, Liu D, Li L, et al. Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues. DCC 2020 [DOI]
Minnen D, Singh S. Channel-wise autoregressive entropy models for learned image compression. ICIP 2020 [DOI]
Yuan L, Luo J, Li S, et al. Learned Image Compression with Channel-Wise Grouped Context Modeling. ICIP 2021 [DOI]
Sun H, Yu L, Katto J. Learned Image Compression with Fixed-point Arithmetic. PCS 2021 [DOI]
Yuan Z, Liu H, Mukherjee D, et al. Block-based Learned Image Coding with Convolutional Autoencoder and Intra-Prediction Aided Entropy Coding. PCS 2021 [DOI]
Li C, Luo J, Dai W, et al. Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized Image Compression. VCIP 2020 [DOI]
Wang I H, Ding J J. Deep Learning Based EBCOT Source Symbol Prediction Technique for JPEG2000 Image Compression Architecture. VCIP 2020 [DOI]
M. Cao et al, Entropy Modeling via Gaussian Process Regression for Learned Image Compression. DCC 2022 [DOI]
A. Said, R. Pourreza and H. Le. Optimized Learned Entropy Coding Parameters for Practical Neural-Based Image and Video Compression. ICIP 2022 [DOI]
X. Fan, W. Fei, W. Dai, C. Li, J. Zou and H. Xiong. Tensor Network-Based Entropy Coding For Learned Image Compression. PCS 2022 [DOI]
Post- or In-Loop Filtering
Kim Y, Soh J W, Park J, et al. A pseudo-blind convolutional neural network for the reduction of compression artifacts. TCSVT 2019 [DOI]
Huang Y W, Hsu C W, Chen C Y, et al. A VVC proposal with quaternary tree plus binary-ternary tree coding block structure and advanced coding techniques. TCSVT 2019 [DOI]
Zhang S, Fan Z, Ling N, et al. Recursive Residual Convolutional Neural Network-Based In-Loop Filtering for Intra Frames. TCSVT 2019 [DOI]
Zheng B, Chen Y, Tian X, et al. Implicit dual-domain convolutional network for robust color image compression artifact reduction. TCSVT 2019 [DOI]
Z. Jin, M. Z. Iqbal, W. Zou, X. Li and E. Steinbach. Dual-stream Multi-path Recursive Residual Network for JPEG Image Compression Artifacts Reduction. TCSVT 2020 [DOI]
H. Huang, I. Schiopu and A. Munteanu. Frame-wise CNN-based Filtering for Intra-Frame Quality Enhancement of HEVC Videos. TCSVT 2020 [DOI]
M. Park, H. G. Kim, S. Lee and Y. M. Ro. Robust Video Frame Interpolation with Exceptional Motion Map. TCSVT 2020 [DOI]
Z. Huang, J. Sun, X. Guo and M. Shang. One-for-All: An Efficient Variable Convolution Neural Network for In-Loop Filter of VVC. TCSVT 2022 [DOI]
Lu G, Zhang X, Ouyang W, et al. Deep Non-Local Kalman Network for Video Compression Artifact Reduction. TIP 2019 [DOI]
Pan Z, Yi X, Zhang Y, et al. Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC. TIP 2020 [DOI]
Liu J, Liu D, Yang W, et al. A Comprehensive Benchmark for Single Image Compression Artifact Reduction. TIP 2020 [DOI]
Zhang X, Wu X. Ultra High Fidelity Deep Image Decompression With l∞-Constrained Compression. TIP 2020 [DOI]
J. Li, Y. Wang, H. Xie and K. Ma. Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal. TIP 2020 [DOI]
H. Choi and I. V. Bajić. Affine Transformation-Based Deep Frame Prediction. TIP 2021 [DOI]
Z. Huang, J. Sun, X. Guo and M. Shang. Adaptive Deep Reinforcement Learning-Based In-Loop Filter for VVC. TIP 2021 [DOI]
Galteri L, Seidenari L, Bertini M, et al. Deep universal generative adversarial compression artifact removal. TMM 2019 [DOI]
Lin W, He X, Han X, et al. Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC. TMM 2019 [DOI]
Xiao W, He H, Wang T, et al. The Interpretable Fast Multi-Scale Deep Decoder for the Standard HEVC Bitstreams. TMM 2020 [DOI]
Chen Y C, Chang K J, Tsai Y H, et al. Learning Patterns of Latent Residual for Improving Video Compression. CVPR Workshops 2019 [url]
Cui K, Steinbach E G. Decoder Side Color Image Quality Enhancement using a Wavelet Transform based 3-stage Convolutional Neural Network. CVPR Workshops 2019 [url]
Li M, Xia C, Hu J, et al. VimicroABCnet: An Image Coder Combining A Better Color Space Conversion Algorithm and A Post Enhancing Network. CVPR Workshops 2019 [url]
Hu J. An Image Coder With CNN Optimizations. CVPR Workshops 2019 [url]
Xue Y, Su J. Attention Based Image Compression Post-Processing Convlutional Neural Network. CVPR Workshops 2019 [url]
Huang C C, Nguyen T P, Lai C T. Learned Prior Information for Image Compression. CVPR Workshops 2019 [url]
Cho S, Lee J, Kim J, et al. Low Bit-rate Image Compression based on Post-processing with Grouped Residual Dense Network. CVPR Workshops 2019 [url]
Lu M, Chen T, Liu H, et al. Learned Image Restoration for VVC Intra Coding. CVPR Workshops 2019 [url]
Hong Lam Y, Zare A, Aytekin C, et al. Compressing Weight-updates for Image Artifacts Removal Neural Networks. CVPR Workshops 2019 [url]
Li M, Zhang Y, Xia C, et al. Improve Image Codec's Performance by Variating Post Enhancing Neural Network: Submission of zxw for CLIC2020. CVPR Workshops 2020 [url]
Tao H, Qian J, Yu L. Post-Processing Network Based on Dense Inception Attention for Video Compression. CVPR Workshops 2020 [url]
Hu Y, Ma H, Liu D, et al. Compression Artifact Removal With Ensemble Learning of Neural Networks. CVPR Workshops 2020 [url]
Wang Z, Liao R L, Ye Y. Joint Learned and Traditional Video Compression for P Frame. CVPR Workshops 2020 [url]
Kim Y, Cho S, Lee J, et al. Towards the Perceptual Quality Enhancement of Low Bit-Rate Compressed Images. CVPR Workshops 2020 [url]
Li X, Sun S, Zhang Z, et al. Multi-scale Grouped Dense Network for VVC Intra Coding. CVPR Workshops 2020 [url]
Pham, Chi DK and Fu, Chen and Zhou, Jinjia. Deep Learning Based Spatial-Temporal In-Loop Filtering for Versatile Video Coding. CVPR Workshops 2021 [url]
Zhimeng Huang, Kai Lin, Chuanmin Jia, Shanshe Wang, Siwei Ma. Beyond VVC: Towards Perceptual Quality Optimized Video Compression Using Multi-Scale Hybrid Approaches. CVPR Workshops 2021 [url]
Fu X, Zha Z J, Wu F, et al. Jpeg artifacts reduction via deep convolutional sparse coding. ICCV 2019 [DOI]
Xu Y, Gao L, Tian K, et al. Non-Local ConvLSTM for Video Compression Artifact Reduction. ICCV 2019 [DOI]
Ehrlich M, Lim S N, Davis L, et al. Quantization Guided JPEG Artifact Correction. ECCV 2020 [url]
Xing Q, Xu M, Li T, et al. Early Exit Or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images. ECCV 2020 [url]
Ayzik S, Avidan S. Deep Image Compression using Decoder Side Information. ECCV 2020 [url]
Deng J, Wang L, Pu S, et al. Spatio-temporal deformable convolution for compressed video quality enhancement. AAAI 2020 [DOI]
Zhang X, Wu X. Near-Lossless ℓ∞-Constrained Image Decompression via Deep Neural Network. DCC 2019 [DOI]
Zhu C, Chen Y, Zhang Y, et al. ResGAN: A Low-Level Image Processing Network to Restore Original Quality of JPEG Compressed Images. DCC 2019 [DOI]
Li T, Xu M, Yang R, et al. A DenseNet based approach for multi-frame in-loop filter in HEVC. DCC 2019 [DOI]
Meng X, Deng X, Zhu S, et al. Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement. DCC 2020 [DOI]
Wang S, Yi P, Wang H, et al. Densely Connected Unit based Loop Filter for Short Video Coding. DCC 2020 [DOI]
Xu X, Qian J, Yu L, et al. Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video Enhancement in HEVC. DCC 2020 [DOI]
Wang Z, Ma C, Liao R L, et al. Multi-Density Convolutional Neural Network for In-Loop Filter in Video Coding. DCC 2021 [DOI]
Huang Z, Guo X, Shang M, et al. An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Coding. DCC 2021 [DOI]
Sun X, Liu P, Jia K, et al. 3D-CVQE: An Effective 3D-CNN Quality Enhancement for Compressed Video Using Limited Coding Information. DCC 2021 [DOI]
Liu Z, Han Y, Qi H, et al. Video Enhancement Network Based on Max-Pooling and Hierarchical Feature Fusion. DCC 2021 [DOI]
Yang R, Sun X, Xu M, et al. Quality-gated convolutional LSTM for enhancing compressed video. ICME 2019 [DOI]
Wang J, Liu S, Jiang F, et al. A Video Post-Filter Deblocking Method Based on Temporal Boosting Residual Networks. ICME 2019 [DOI]
Yao J, Wang L, Chen F, et al. An attention residual neural network with recurrent greedy approach as loop filter for inter frames. ICME 2019 [DOI]
Huang Z, Li Y, Sun J. Multi-Gradient Convolutional Neural Network Based In-Loop Filter For Vvc. ICME 2020 [DOI]
Zhang F, Feng C, Bull D R. Enhancing VVC through CNN-based Post-Processing. ICME 2020 [DOI]
Gao S, Xiong Z. Deep Enhancement for 3D HDR Brain Image Compression. ICIP 2019 [DOI]
Meng X, Deng X, Zhu S, et al. Enhancing quality for VVC compressed videos by jointly exploiting spatial details and temporal structure. ICIP 2019 [DOI]
Wang D, Xia S, Yang W, et al. Partition tree guided progressive rethinking network for in-loop filtering of HEVC. ICIP 2019 [DOI]
Esmaeilzehi A, Ahmad M O, Swamy M N S. Deep Jpeg Image Deblocking Using Residual Maxout Units. ICIP 2019 [DOI]
Li B, Liang J, Wang Y. Compression Artifact Removal with Stacked Multi-Context Channel-Wise Attention Network. ICIP 2019 [DOI]
Kim T, Lee H, Son H, et al. SF-CNN: A Fast Compression Artifacts Removal via Spatial-To-Frequency Convolutional Neural Networks. ICIP 2019 [DOI]
Xia J, Wen J. Asymmetric Convolutional Residual Network for AV1 Intra in-Loop Filtering. ICIP 2020 [DOI]
Esmaeilzehi A, Ahmad M O, Swamy M N S. Development Of New Fractal And Non-Fractal Deep Residual Networks For Deblocking Of Jpeg Decompressed Images. ICIP 2020 [DOI]
Jia W, Li L, Li Z, et al. Residual Guided Deblocking With Deep Learning. ICIP 2020 [DOI]
Li Y, Zhang L, Zhang K. Convolutional Neural Network Based In-Loop Filter For VVC Intra Coding. ICIP 2021 [DOI]
Yu Y, Yang X, Chen J, et al. Deep Learning Based In-Loop Filter for Video Coding. VCIP 2019 [DOI]
Ma H, Liu D, Wu F. Improving Compression Artifact Reduction via End-to-End Learning of Side Information. VCIP 2020 [DOI]
Nasiri F, Hamidouche W, Morin L, et al. Prediction-Aware Quality Enhancement of VVC Using CNN. VCIP 2020 [DOI]
Yue J, Gao Y, Li S, et al. A Mixed Appearance-based and Coding Distortion-based CNN Fusion Approach for In-loop Filtering in Video Coding. VCIP 2020 [DOI]
J. Qian, H. Wang and L. Yu. Distortion-based Neural Network for Compression Artifacts Reduction in VVC. VCIP 2021 [DOI]
Hoang T M, Zhou J. B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reduction. PCS 2019 [DOI]
Yi P, Wang S, Wang H, et al. Simplified Inception Unit based Filter for HEVC. PCS 2019 [DOI]
Bonnineau C, Hamidouche W, Travers J F, et al. Multitask Learning for VVC Quality Enhancement and Super-Resolution. PCS 2021 [DOI]
Bordes P, Galpin F, Dumas T, et al. Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks. PCS 2021 [DOI]
Nasiri F, Hamidouche W, Morin L, et al. Model Selection CNN-based VVC QualityEnhancement. PCS 2021 [DOI]
Convolutional neural network-based post-filtering for compressed YUV420 images and video. PCS 2021 [DOI]
J. Li, Y. Li, K. Zhang and L. Zhang, Joint Rate Distortion Optimization with CNN-based In-Loop Filter For Hybrid Video Coding. DCC 2022 [DOI]
X. Meng et al, Parametric Non-local In-loop Filter for Future Video Coding. DCC 2022 [DOI]
Z. Liu, Y. Duan and M. Zhang, An Improved Multi-reference Frame Loop Filter Algorithm Based on Transformer for VVC. DCC 2022 [DOI]
Tong Shao,Tianqi Liu, Dapeng Wu, Chia-Yang Tsai, Zhijun Lei, Ioannis Katsavounidis.PTR-CNN for in-loop filtering in video coding. JVCIR 2022 [DOI]
Y. Fu, S. Wang, C. Zhu, L. Song and W. Zhang. An Attention Based CNN with Temporal Hierarchical Deployment for AVS3 Inter In-loop Filtering. ISCAS 2022 [DOI]
W. Bayliss, L. Murn, E. Izquierdo, Q. Zhang and M. Mrak. Complexity Reduction of Learned In-Loop Filtering in Video Coding. ISCAS 2022 [DOI]
C. Liu, H. Sunyz, J. Kattoz, X. Zeng and Y. Fan. A QP-adaptive Mechanism for CNN-based Filter in Video Coding. ISCAS 2022 [DOI]
Y. Zhao, K. Lin, S. Wang and S. Ma. Joint Luma and Chroma Multi-Scale CNN In-loop Filter for Versatile Video Coding. ISCAS 2022 [DOI]
T. Shao, J. N. Shingala, P. Yin, A. Arora, A. Shyam and S. McCarthy. A Low Complexity Convolutional Neural Network with Fused CP Decomposition for In-Loop Filtering in Video Coding. DCC 2023 [DOI]
B. Kathariya, Z. Li, H. Wang and G. Van Der Auwera. Multi-stage Locally and Long-range Correlated Feature Fusion for Learned In-loop Filter in VVC. VCIP 2022 [DOI]
B. Kathariya, Z. Li, H. Wang and M. Coban. Multi-Stage Spatial and Frequency Feature Fusion using Transformer in CNN-Based In-Loop Filter for VVC. PCS 2022 [DOI]
L. Wang, X. Xu and S. Liu. Optimize neural network based in-loop filters through iterative training. PCS 2022 [DOI]
Down- and Up-Sampling
Bourtsoulatze E, Chadha A, Fadeev I, et al. Deep video precoding. TCSVT 2019 [DOI]
G. Li, J. Lei, Z. Pan, B. Peng and N. Ling. Multiple Resolution Prediction With Deep Up-Sampling for Depth Video Coding. TCSVT 2022 [DOI]
Ho M M, Zhou J, He G. RR-DnCNN v2. 0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling-Based Video Coding. TIP 2020 [DOI]
Lin H, He X, Qing L, et al. Improved low-bitrate HEVC video coding using deep learning based super-resolution and adaptive block patching. TMM 2019 [DOI]
Savioli N. A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression. CVPR Workshops 2019 [url]
Bonnineau C, Hamidouche W, Travers J F, et al. Versatile video coding and super-resolution for efficient delivery of 8K video with 4K backward-compatibility. ICASSP 2020 [DOI]
Yu S, Tong X, Huang Y, et al. Learning-Based Quality Enhancement For Scalable Coded Video Over Packet Lossy Networks. ICME 2020 [DOI]
Zhang F, Afonso M, Bull D R. Enhanced video compression based on effective bit depth adaptation. ICIP 2019 [DOI]
Y. Wei, L. Chen and L. Song. Video Compression based on Jointly Learned Down-Sampling and Super-Resolution Networks. VCIP 2021 [DOI]
C. Lin, Y. Li, K. Zhang, Z. Zhang and L. Zhang. CNN-based Super Resolution for Video Coding Using Decoded Information. VCIP 2021 [DOI]
S. Huang, C. Jung, Y. Liu and M. Li. CNN Filter for Super-Resolution with RPR Functionality in VVC. ICASSP 2023 [DOI]
H. Lan, C. Jung, Y. Liu and M. Li. CNN Filter for RPR-Based SR in VVC with Wavelet Decomposition. ICASSP 2023 [DOI]
M. Guo, S. Zhao, H. Jiang, J. Li and L. Zhang. Video Compression with Arbitrary Rescaling Network. DCC 2023 [DOI]
K. He, C. Fu, C. D. -K. Pham, L. Zhang and J. Zhou. Temporal Down-sampling based Video Coding with Frame-Recurrent Enhancement. DCC 2023 [DOI]
Encoding Optimizations
Kim K, Ro W W. Fast CU depth decision for HEVC using neural networks. TCSVT 2018 [DOI]
Han C, Duan Y, Tao X, et al. Toward Variable-Rate Generative Compression by Reducing the Channel Redundancy. TCSVT 2020 [DOI]
Kuang W, Chan Y L, Tsang S H, et al. DeepSCC: Deep learning based fast prediction network for screen content coding. TCSVT 2019 [DOI]
Hong W, Chen T, Lu M, et al. Efficient Neural Image Decoding via Fixed-Point Inference. TCSVT 2020 [DOI]
Zhang H, Li J, Li B, et al. A Deep Reinforcement Learning Approach to Multiple Streams’ Joint Bitrate Allocation. TCSVT 2020 [DOI]
C. Gao, L. Li, D. Liu, Z. Chen, W. Li and F. Wu. Two-Step Fast Mode Decision for Intra Coding of Screen Content. TCSVT 2022 [DOI]
S. Wu, J. Shi and Z. Chen. HG-FCN: Hierarchical Grid Fully Convolutional Network for Fast VVC Intra Coding. TCSVT 2022 [DOI]
Liu H, Zhang Y, Zhang H, et al. Deep learning-based picture-wise just noticeable distortion prediction model for image compression. TIP 2019 [DOI]
Chen Z, Shi J, Li W. Learned fast HEVC intra coding. TIP 2020 [DOI]
Klopp J P, Chen L G, Chien S Y. Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding. TIP 2020 [DOI]
Li T, Xu M, Deng X, et al. Accelerate CTU Partition to Real Time for HEVC Encoding With Complexity Control[J]. IEEE Transactions on Image Processing. TIP 2020 [DOI]
Paul S, Norkin A, Bovik A C. Speeding Up VP9 Intra Encoder With Hierarchical Deep Learning-Based Partition Prediction. TIP 2020 [DOI]
Wang T, Li F, Qiao X, et al. Low-Complexity Error Resilient HEVC Video Coding: A Deep Learning Approach. TIP 2020 [DOI]
Park S, Kang J. Fast Multi-type Tree Partitioning for Versatile Video Coding Using a Lightweight Neural Network. TMM 2020 [DOI]
Kwong S, Zhou M, Xuekai W E I, et al. Rate Control Method Based on Deep Reinforcement Learning for Dynamic Video Sequences in HEVC. TMM 2020 [DOI]
S. -h. Park and J. -W. Kang. Fast Multi-Type Tree Partitioning for Versatile Video Coding Using a Lightweight Neural Network. TMM 2020 [DOI]
X. Dong, L. Shen, M. Yu and H. Yang. Fast Intra Mode Decision Algorithm for Versatile Video Coding. TMM 2021 [DOI]
Li N, Zhang Y, Zhu L, et al. Reinforcement learning based coding unit early termination algorithm for high efficiency video coding. JVCIR 2019 [DOI]
Cai C, Lu G, Hu Q, et al. Efficient Learning Based Sub-pixel Image Compression. CVPR Workshops 2019 [url]
Yang Y, Bamler R, Mandt S. Improving inference for neural image compression. NeurIPS 2020 [url]
Shi J, Gao C, Chen Z. Asymmetric-Kernel CNN Based Fast CTU Partition for HEVC Intra Coding. ISCAS 2019 [DOI]
Kuang W, Chan Y L, Tsang S H. Low-Complexity Intra Prediction for Screen Content Coding by Convolutional Neural Network. ISCAS 2020 [DOI]
Chen T, Ma Z. Variable Bitrate Image Compression with Quality Scaling Factors. ICASSP 2020 [DOI]
Galpin F, Racapé F, Jaiswal S, et al. CNN-Based Driving of Block Partitioning for Intra Slices Encoding. DCC 2019 [DOI]
Zhang Y, Wang G, Tian R, et al. Texture-classification accelerated CNN scheme for fast intra CU partition in HEVC. DCC 2019 [DOI]
Lin T L, Liang K W, Huang J Y, et al. Intra mode prediction for H. 266/FVC video coding based on convolutional neural network. DCC 2020 [DOI]
Lu X, Zhou B, Jin X, et al. A Rate Control Scheme for HEVC Intra Coding Using Convolution Neural Network (CNN). DCC 2020 [DOI]
Tech G, Pfaff J, Schwarz H, et al. Fast Partitioning for VVC Intra-Picture Encoding with a CNN Minimizing the Rate-Distortion-Time Cost. DCC 2021 [DOI]
Ho Y H, Jin G L, Liang Y, et al. A Dual-Critic Reinforcement Learning Framework for Frame-Level Bit Allocation in HEVC/H. 265. DCC 2021 [DOI]
Han X, Wang S, Chen Y, et al. Intra Block Partition Structure Prediction via Convolutional Neural Network. DCC 2021 [DOI]
Li Y, Zhang L, Xu J. Convolutional Neural Network-based Split Prediction for VVC Intra Speedup. DCC 2021 [DOI]
Lin J, Liu D, Liang J, et al. Modulated Variable-Rate Deep Video Compression. DCC 2021 [DOI]
Feng A, Gao C, Li L, et al. Cnn-Based Depth Map Prediction for Fast Block Partitioning in HEVC Intra Coding. ICME 2021 [DOI]
Zhao, Jing and Li, Bin and Li, Jiahao and Xiong, Ruiqin and Lu, Yan. A Universal Encoder Rate Distortion Optimization Framework for Learned Compression. CVPR Workshops 2021 [url]
Nannan Zou, Honglei Zhang, Francesco Cricri, Hamed R. Tavakoli, Jani Lainema, Emre Aksu, Miska Hannuksela, Esa Rahtu. Learned Video Compression With Intra-Guided Enhancement and Implicit Motion Information. CVPR Workshops 2021 [url]
Feng L, Zhang X, Wang S, et al. Coding prior based high efficiency restoration for compressed video. ICIP 2019 [DOI]
Zhao L, Wei Z, Cai W, et al. Efficient screen content coding based on convolutional neural network guided by a large-scale database. ICIP 2019 [DOI]
Su H, Tsai C Y, Wang Y, et al. Machine learning accelerated partition search for video encoding. ICIP 2019 [DOI]
Sun Y, Li L, Li Z, et al. Yoco: Light-Weight Rate Control Model Learning. ICIP 2020 [DOI]
Kirmemis O, Tekalp A M. Shrinkage as Activation for Learned Image Compression. ICIP 2020 [DOI]
Sun H, Cheng Z, Takeuchi M, et al. End-To-End Learned Image Compression With Fixed Point Weight Quantization. ICIP 2020 [DOI]
Dardouri T, Kaaniche M, Benazza-Benyahia A, et al. Optimized Lifting Scheme Based on A Dynamical Fully Connected Network for Image Coding. ICIP 2020 [DOI]
Tissier A, Hamidouche W, Vanney J, et al. CNN Oriented Complexity Reduction Of VVC Intra Encoder. ICIP 2020 [DOI]
Tech G, Pfaff J, Schwarz H, et al. CNN-based parameter selection for fast VVC intra-picture encoding. ICIP 2021 [DOI]
Lin J, Liu D, Liang J, et al. A Deeply Modulated Scheme for Variable-Rate Video Compression. ICIP 2021 [DOI]
Schäfer M, Pientka S, Pfaff J, et al. Rate-Distortion-Optimization for Deep Image Compression. ICIP 2021 [DOI]
Wang S, Zhang Y, Yang D, et al. SSIM Prediction for H. 265/HEVC based on Convolutional Neural Networks. VCIP 2019 [DOI]
Tang G, Jing M, Zeng X, et al. Adaptive CU Split Decision with Pooling-variable CNN for VVC Intra Encoding. VCIP 2019 [DOI]
Tsang S H, Kwong N W, Chan Y L. FastSCCNet: Fast Mode Decision in VVC Screen Content Coding via Fully Convolutional Network. VCIP 2020 [DOI]
Ren W, Su J, Sun C, et al. An IBP-CNN Based Fast Block Partition For Intra Prediction. PCS 2019 [DOI]
Huang Y, Song L, Izquierdo E. CNN Accelerated Intra Video Coding, Where Is the Upper Bound?. PCS 2019 [DOI]
Su H, Chen M, Bokov A, et al. Machine Learning Accelerated Transform Search For AV1. PCS 2019 [DOI]
Xing H, Zhou Z, Wang J, et al. Predicting Rate Control Target Through A Learning Based Content Adaptive Model. PCS 2019 [DOI]
Ringis D J, Pitié F, Kokaram A. Near optimal per-clip lagrangian multiplier prediction in hevc. PCS 2021 [DOI]
Tech G, Pfaff J, Schwarz H, et al. Rate-Distortion-Time Cost Aware CNN Training for Fast VVC Intra-Picture Partitioning Decisions. PCS 2021 [DOI]
J. Zhang et al, Fast Partition Mode Decision via a Plug-in Fully Connected Network for Video Coding. DCC 2022 [DOI]
F. Brand, K. Fischer, A. Kopte and A. Kaup, Learning True Rate-Distortion-Optimization for End-To-End Image Compression. DCC 2022 [DOI]
S. Zvezdakov, A. Solovyov and D. Vatolin, Iterative Machine-Learning-Based Method of Selecting Encoder Parameters for Speed-Bitrate Tradeoff. DCC 2022 [DOI]
Z. Zhang, L. Yu, J. Qian and H. Wang, Learning-Based Fast Depth Inter Coding for 3D-HEVC via XGBoost. DCC 2022 [DOI]
Jiunn-Tsair Fang, Bang-Hao Liu, Pao-Chi Chang. Fast coding unit partitioning algorithms for versatile video coding intra coding. JVCIR 2022 [DOI]
F. Sagrilo, M. Loose, R. Viana, G. Sanchez, G. Corrêa and L. Agostini. Learning-Based Fast VVC Affine Motion Estimation. ISCAS 2023 [DOI]
C. Shu, C. Yang and P. An. An Online SVM Based VVC Intra Fast Partition Algorithm With Pre-Scene-cut Detection. ISCAS 2023 [DOI]
A. Duarte, B. Zatt, G. Correa and D. Palomino. Fast Intra Mode Decision Using Machine Learning for the Versatile Video Coding Standard. ISCAS 2023 [DOI]
R. Wang, Q. Mao, C. Jia, R. Wang and S. Ma. Extreme Generative Human-Oriented Video Coding via Motion Representation Compression. ISCAS 2023 [DOI]
A. Meyer and A. Kaup. A Novel Cross-Component Context Model for End-to-End Wavelet Image Coding. ICASSP 2023 [DOI]
M. Abdoli, G. Clare and F. Henry. GOP-Based Latent Refinement for Learned Video Coding. ICASSP 2023 [DOI]
L. R. Duong, B. Li, C. Chen and J. Han. Multi-Rate Adaptive Transform Coding for Video Compression. ICASSP 2023 [DOI]
F. Lin, H. Sun, J. Liu and J. Katto. Multistage Spatial Context Models for Learned Image Compression. ICASSP 2023 [DOI]
H. Zhong, J. Xu, C. Zhu, D. Feng and L. Song. Complexity-Oriented Per-Shot Video Coding Optimization. ICME 2022 [DOI]
S. Wang and H. Wang. Discretized Gaussian Mixture Hyperprior for Learned Image Compression with Mask Module. ICME 2022 [DOI]
L. Li, Z. Yang, Y. Zhai, J. Yang and R. Wang. Improving Multi-generation Robustness of Learned Image Compression. ICME 2023 [DOI]
S. Yin et al. Exploring Structural Sparsity in Neural Image Compression. ICIP 2022 [DOI]
L. Yu, W. Chang, Q. Liu and M. Gabbouj. High-frequency guided CNN for video compression artifacts reduction. VCIP 2022 [DOI]
X. Shi, J. Lin, D. Jiang, C. Nian and J. Yin. Recurrent Network with Enhanced Alignment and Attention-Guided Aggregation for Compressed Video Quality Enhancement. VCIP 2022 [DOI]
A. Harell, A. De Andrade and I. V. Bajić. Rate-Distortion in Image Coding for Machines. PCS 2022 [DOI]
B. Azizian and I. V. Bajić. Privacy-Preserving Feature Coding for Machines. PCS 2022 [DOI]
Point Cloud Compression
R Song, C Fu, S Liu, G Li. Efficient Hierarchical Entropy Model for Learned Point Cloud Compression. CVPR 2023 [DOI]
C Fu, G Li, R Song, W Gao, S Liu. OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression. AAAI 2022 [url]
Mingyue Cui, Junhua Long, Mingjian Feng, Boyang Li, Huang Kai. OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression with Local Enhancement. AAAI 2023 [url]
Xuhao Jiang, Weimin Tan, Tian Tan, Bo Yan, Liquan Shen. Multi-Modality Deep Network for Extreme Learned Image Compression. AAAI 2023 [url]
D. T. Nguyen, M. Quach, G. Valenzise and P. Duhamel. Lossless Coding of Point Cloud Geometry Using a Deep Generative Model. TCSVT 2021 [DOI]
D. Ding, C. Qiu, F. Liu and Z. Pan. Point Cloud Upsampling via Perturbation Learning. TCSVT 2021 [DOI]
J. Wang, H. Zhu, H. Liu and Z. Ma. Lossy Point Cloud Geometry Compression via End-to-End Learning. TCSVT 2021 [DOI]
X. Li, W. Dai, S. Li, C. Li, J. Zou and H. Xiong. Graph Dictionary Learning for 3-D Point Cloud Compression. DCC 2022 [DOI]
He, Yun and Ren, Xinlin and Tang, Danhang and Zhang, Yinda and Xue, Xiangyang and Fu, Yanwei. Density-Preserving Deep Point Cloud Compression. CVPR 2022 [url]
Fang, Guangchi and Hu, Qingyong and Wang, Hanyun and Xu, Yiling and Guo, Yulan. 3DAC: Learning Attribute Compression for Point Clouds CVPR 2022 [url]
W. Jia, L. Li, A. Akhtar, Z. Li and S. Liu. Convolutional Neural Network-Based Occupancy Map Accuracy Improvement for Video-Based Point Cloud Compression. TMM 2021 [DOI]
X. Sheng, L. Li, D. Liu, Z. Xiong, Z. Li and F. Wu. Deep-PCAC: An End-to-End Deep Lossy Compression Framework for Point Cloud Attributes. TMM 2021 [DOI]
A. Akhtar, W. Gao, L. Li, Z. Li, W. Jia and S. Liu. Video-Based Point Cloud Compression Artifact Removal. TMM 2021 [DOI]
T. Wang, F. Li and P. C. Cosman. Learning-Based Rate Control for Video-Based Point Cloud Compression. TIP 2022 [DOI]
X. Sheng, L. Li, D. Liu and Z. Xiong. Attribute Artifacts Removal for Geometry-Based Point Cloud Compression. TIP 2022 [DOI]
J. Ahn, J. Pang, M. A. Lodhi and D. Tian. DDA-Net: Deep Distribution-Aware Network for Point Cloud Compression. ISCAS 2023 [DOI]
Y. Li, Q. Yang, W. Dai, C. Li, J. Zou and H. Xiong. Rotation-Invariant Point Cloud Segmentation With Kernel Principal Component Analysis and Geometry-Based Weighted Convolution. ISCAS 2023 [DOI]
R. B. Pinheiro, J. -E. Marvie, G. Valenzise and F. Dufaux. NF-PCAC: Normalizing Flow Based Point Cloud Attribute Compression. ICASSP 2023 [DOI]
Y. Lin, T. Xu, Z. Zhu, Y. Li, Z. Wang and Y. Wang. Your Camera Improves Your Point Cloud Compression. ICASSP 2023 [DOI]
T. T. Do, P. A. Chou and G. Cheung. Volumetric Attribute Compression for 3D Point Clouds Using Feedforward Network with Geometric Attention. ICASSP 2023 [DOI]
M. A. Lodhi, J. Pang and D. Tian. Sparse Convolution Based Octree Feature Propagation for Lidar Point Cloud Compression. ICASSP 2023 [DOI]
C. -S. Liu, J. -F. Yeh, H. Hsu, H. -T. Su, M. -S. Lee and W. H. Hsu. BIRD-PCC: Bi-Directional Range Image-Based Deep Lidar Point Cloud Compression. ICASSP 2023 [DOI]
D. T. Nguyen, K. G. Nambiar and A. Kaup. Deep Probabilistic Model for Lossless Scalable Point Cloud Attribute Compression. ICASSP 2023 [DOI]
J. Wang, D. Ding and Z. Ma. Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction. DCC 2023 [DOI]
P. Chen, S. Wang and Z. Li. Occupancy Map Guided Attributes Deblocking for Video-based Point Cloud Compression. DCC 2023 [DOI]
X. Zhang, G. Liao, W. Gao and G. Li. TDRNet: Transformer-Based Dual-Branch Restoration Network for Geometry Based Point Cloud Compression Artifacts. ICME 2022 [DOI]
Y. Chen, J. Wang and G. Li. A efficient predictive wavelet transform for LiDAR point cloud attribute compression. VCIP 2022 [DOI]
G. Liu, J. Wang, D. Ding and Z. Ma. PCGFormer: Lossy Point Cloud Geometry Compression via Local Self-Attention. VCIP 2022 [DOI]
S. -Y. Li, J. -J. Chiu, J. -C. Chiang, W. -H. Peng and W. -N. Lie. Augmented Normalizing Flow for Point Cloud Geometry Coding. VCIP 2022 [DOI]
D. Hu, S. Chen, H. Yang and G. Wang. Distribution-aware Low-bit Quantization for 3D Point Cloud Networks. VCIP 2022 [DOI]
Y. Hu and Y. Wang. Learning Neural Volumetric Field for Point Cloud Geometry Compression. PCS 2022 [DOI]
NERF/NERV
L Li, Z Shen, Z Wang, L Shen, L Bo. Compressing Volumetric Radiance Fields to 1 MB. CVPR 2023 [DOI]
Bo He, Xitong Yang, Hanyu Wang, Zuxuan Wu, Hao Chen, Shuaiyi Huang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava. Towards Scalable Neural Representation for Diverse Videos. CVPR 2023 [DOI]
Runzhao Yang. TINC: Tree-Structured Implicit Neural Compression. CVPR 2023 [DOI]
S Girish, A Shrivastava, K Gupta. SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations. ICCV 2023 [url]
Yannick Strümpler, Janis Postels, Ren Yang, Luc Van Gool, Federico Tombari. Implicit Neural Representations for Image Compression. ECCV 2022 [DOI]
Tianli Zhao, Jiayuan Chen, Cong Leng, Jian Cheng. Compressible-composable NeRF via Rank-residual Decomposition. NeurIPS 2022 [url]
Jonathan Richard Schwarz, Jihoon Tack, Yee-Whye Teh, Jaeho Lee, Jinwoo Shin. Modality-Agnostic Variational Compression of Implicit Neural Representations. ICML 2023 [url]
Tianli Zhao, Jiayuan Chen, Cong Leng, Jian Cheng. TinyNeRF: Towards 100 x Compression of Voxel Radiance Fields. AAAI 2023 [url]