Recent papers and codes related to deep learning/deep neural network based image compression and video coding framework.

• [Google] George Toderici, Sean M. O’Malley, Sung Jin Hwang, Damien Vincent, David Minnen, Shumeet Baluja, Michele Covell & Rahul Sukthankar: Variable Rate Image Compression with Recurrent Neural Networks. ICLR 2016. [paper]
• [DeepMind] Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra: Towards conceptual compression. NIPS 2016. [paper]

• [Google] George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele Covell: Full Resolution Image Compression with Recurrent Neural Networks. CVPR 2017. [paper]
• [HIT] Feng Jiang, Wen Tao, Shaohui Liu, Jie Ren, Xun Guo, Debin Zhao: An End-to-End Compression Framework Based on Convolutional Neural Networks. Trans CSVT. [paper]
• [NYU] J. Ballé, V. Laparra, E. P. Simoncelli: End-to-end optimized image compression. ICLR 2017.[paper]
• [Twitter] L. Theis, W. Shi, A. Cunningham, F. Huszár: Lossy image compression with compressive autoencoders. ICLR 2017. [paper]
• [INRIA] T. Dumas, A. Roumy, C. Guillemot: Image compression with stochastic winner-take-all auto-encoder. ICASSP 2017. [paper]
• [WaveOne] O. Rippel, L. Bourdev: Real-time adaptive image compression. ICML 2017. [paper]
• [Dartmouth] M. H. Baig, V. Koltun, L. Torresani: Learning to Inpaint for Image Compression. NIPS 2017. [paper]
• [BJTU] Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao: Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image. Arxiv. [paper]

• [NJU] Tong Chen, Haojie Liu, Qiu Shen, Tao Yue: DeepCoder: A deep neural network based video compression. VCIP 2017. [paper]

• [Google] N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. J. Hwang, J. Shor, G. Toderici: Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks. CVPR 2018. [paper]
• [HKPU] M. Li, W. Zuo, S. Gu, D. Zhao, D. Zhang: Learning convolutional networks for content-weighted image compression. CVPR 2018. [paper]
• [Google] David Minnen, Johannes Ballé, George Toderici: Joint Autoregressive and Hierarchical Priors for Learned Image Compression. NIPS 2018. [paper]
• [Google] Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston: Variational image compression with a scale hyperprior. ICLR 2018. [paper]
• [ETHZ] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, L. Van Gool: Conditional probability models for deep image compression. CVPR 2018. [paper]
• [Technion] T.R. Shaham, T. Michaeli: Deformation Aware Image Compression. CVPR 2018. [paper]
• [INRIA] T. Dumas, Aline, Roumy, C. Guillemot: Autoencoder based Image Compression: Can the Learning be Quantization Independent? ICASSP 2018. [paper]
• [BJTU] Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao: Multiple Description Convolutional Neural Networks for Image Compression. Trans CSVT. [paper]
• [SJTU] Chunlei Cai, Li Chen, Xiaoyun Zhang, Zhiyong Gao: Efficient Variable Rate Image Compression With Multi-Scale Decomposition Network. Tans CSVT. [paper]
• [Google] D. Minnen, G. Toderici, S. Singh, S. J. Hwang, M. Covell: Image-Dependent Local Entropy Models for Learned Image Compression. ICIP 2018. [paper]
• [Google] T. Chinen, J. Ballé, C. Gu, S. J. Hwang, S. Ioffe, N. Johnston, T. Leung, D. Minnen, S. O'Malley, C. Rosenberg, G. Toderici Towards A Semantic Perceptual Image Metric. ICIP 2018. [paper]
• [RIT/PSU] A. G. Ororbia, A. Mali, J. Wu, S. O'Connell, D. Miller, C. L. Giles: Learned Neural Iterative Decoding for Lossy Image Compression Systems. ArXiv. [paper]
• [SFU/Google] M. Akbari, J. Liang, J. Han: DSSLIC: Deep Semantic Segmentation-based Layered Image Compression. ArXiv. [paper]
• [ETHZ] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, L. V. Gool: Practical Full Resolution Learned Lossless Image Compression. ArXiv. [paper]

• [USTC] Z. Chen, T. He, X. Jin, F. Wu: Learning for video compression. IEEE Trans. on CSVT 2018. [paper]
• [UTEXAS] C. Wu, N. Singhal, P. Krähenbühl: Video Compression through Image Interpolation. ECCV 2018. [paper]
• [Disney] J. Han, S. Lombardo, C. Schroers, S. Mandt: Deep Probabilistic Video Compression. ArXiv. [paper]
• [SJTU/Sydney] G. Lu, W. Ouyang, D. Xu, X. Zhang, C. Cai, Z. Gao: DVC: An End-to-end Deep Video Compression Framework. ArXiv. [paper]
• [UTEXAS] S. Kim, J. S. Park, C. G. Bampis, J. Lee, M. K. Markey, A. G. Dimakis, A. C. Bovik: Adversarial Video Compression Guided by Soft Edge Detection. ArXiv. [paper]
• [BUAA] Ren Yang, Mai Xu, Zulin Wang, Tianyi Li: Multi-Frame Quality Enhancement for Compressed Video. CVPR 2018. [paper]

• [CAS] Xiaojun Jia, Xingxing Wei, Xiaochun Cao, Hassan Foroosh: ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples. CVPR 2019. [paper]
• [FIU] Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, Wujie Wen: Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples. CVPR 2019. [paper]
• [SoC R&D] Choi, Yoojin，El-Khamy, Mostafa，Lee, Jungwon: Variable Rate Deep Image Compression With a Conditional Autoencoder. ICCV 2019. [paper]
• [ETRI] Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack: Context-adaptive Entropy Model for End-to-end Optimized Image Compression. ICLR 2019. [paper]
• [NJU] Tong Chen, Haojie Liu, Zhan Ma, Qiu Shen, Xun Cao, Yao Wang: Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling. Arxiv. [paper]
• [Waseda University] Song Zebang, Kamata Sei-ichiro: Densely connected AutoEncoders for image compression. ICIGP 2019. [paper]
• [Waseda University] Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto: Energy Compaction-Based Image Compression Using Convolutional AutoEncoder. Trans MM. [paper]
• [ETH Zurich] Maurice Weber, Cedric Renggli, Helmut Grabner, Ce Zhang: Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy. Arxiv. [paper]
• [VUB] Ionut Schiopu, Adrian Munteanu: Deep-learning based Lossless Image Coding. Trans CSVT. [paper]
• [Waseda University] Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto: Learning Image and Video Compression through Spatial-Temporal Energy Compaction. CVPR 2019. [paper]
• [NJU] Haojie Liu, Tong Chen, Peiyao Guo, Qiu Shen, Zhan Ma: Gated Context Model with Embedded Priors for Deep Image Compression. Arxiv. [paper]

• [Dartmouth] Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt: Deep Generative Video Compression. NIPS 2019. [paper]
• [FUDAN] Yi Xu, Longwen Gao, Kai Tian, Shuigeng Zhou, Huyang Sun: Non-Local ConvLSTM for Video Compression Artifact Reduction. ICCV 2019. [paper]
• [WaveOne] O. Rippel, S. Nair, C. Lew, S. Branson, A. G. Anderson, L. Bourdev: Learned Video Compression. ICCV 2019. [paper]
• [SFU] Hyomin Choi, Ivan V. Bajić: Deep Frame Prediction for Video Coding. Trans CSVT. [paper]
• [University of Bristol] Fan Zhang, Mariana Afonso, David Bull: Enhanced Video Compression Based on Effective Bit Depth Adaptation. ICIP 2019. [paper]
• [DisneyResearch] Abdelaziz Djelouah ; Joaquim Campos ; Simone Schaub-Meyer ; Christopher Schroers： Neural Inter-Frame Compression for Video Coding. ICCV 2019. [paper]
• [Qualcomm AI Research] Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco S. Cohen: Video Compression With Rate-Distortion Autoencoders. ICCV 2019. [paper]
• [NUIST] Zhaoqing Pan, Feng Yuan, Jianjun Lei, Sam Kwong: Video Compression Coding via Colorization: A Generative Adversarial Network (GAN)-Based Approach. Arxiv. [paper]
• [USTC] Dong Liu, Yue Li, Jianping Lin, Houqiang Li, Feng Wu: Deep Learning-Based Video Coding: A Review and A Case Study. Arxiv. [paper]
• [KAIST] Woonsung Park, Munchurl Kim: Deep Predictive Video Compression with Bi-directional Prediction. Arxiv. [paper]
• [Qualcomm] Hilmi E. Egilmez, Yung-Hsuan Chao, Antonio Ortega: Graph-based Transforms for Video Coding. Arxiv. [paper]
• [MSU] Vitaliy Lyudvichenko, Mikhail Erofeev, Alexander Ploshkin, Dmitriy Vatolin: Improving Video Compression with Deep Visual-attention Models. IMIP 2019. [paper]
• [university of bristol] Fan Zhang, Mariana Afonso, David R. Bull: ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation. Arxiv. [paper]

• [PKU] Yueyu Hu,Wenhan Yang, Jiaying Liu: Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression. AAAI 2020. [paper]
• [U-Tokyo] Zhisheng Zhong, Hiroaki Akutsu, Kiyoharu Aizawa: Channel-Level Variable Quantization Network for Deep Image Compression. IJCAI 2020. [paper]
• [BUAA] Jiaheng Liu, Guo Lu, Zhihao Hu, Dong Xu: A Unified End-to-End Framework for Efficient Deep Image Compression. Arxiv. [paper]
• [UT-Austin] Li-Heng Chen, Christos G. Bampis, Zhi Li, Andrey Norkin, Alan C. Bovik: Perceptually Optimizing Deep Image Compression. Arxiv. [paper]
• [NJU] Haojie Liu, Han shen, Lichao Huang, Ming Lu, Tong Chen, Zhan Ma: Learned Video Compression via Joint Spatial-Temporal Correlation Exploration. AAAI 2020. [paper]
• [UCL] James Townsend, Thomas Bird, Julius Kunze, David Barber: HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models. ICLR 2020. [paper]
• [Waseda University] Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto: Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules. Arxiv. [paper]
• [UT-Austin] Sheng Cao, Chao-Yuan Wu, Philipp Krähenbühl: Lossless Image Compression through Super-Resolution. Arxiv. [paper]
• [UofT] Yaolong Wang, Mingqing Xiao, Chang Liu, Shuxin Zheng, Tie-Yan Liu: Modeling Lost Information in Lossy Image Compression. Arxiv. [paper]
• [Yonsei University] Hanbin Son, Taeoh Kim, Hyeongmin Lee, and Sangyoun Lee: Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks. Arxiv. [paper]
• [PKU] Jianhui Chang, Zhenghui Zhao, Chuanmin Jia, Shiqi Wang, Lingbo Yang, Jian Zhang and Siwei Ma: Conceptual Compression via Deep Structure and Texture Synthesis. Arxiv [paper]
• [SFU] Mohammad Akbari, Jie Liang, Jingning Han, Chengjie Tu: Learned Multi-Resolution Variable Rate Image Compression with Octave-based Residual Residual Blocks. [paper]

• [USTC] Jianping Lin, Dong Liu, Houqiang Li, Feng Wu: M-LVC: Multiple Frames Prediction for Learned Video Compression. CVPR 2020. [paper]
• [ETH Zurich] Ren Yang, Luc Van Gool, Radu Timofte: OpenDVC: An Open Source Implementation of the DVC Video Compression Method. Arxiv. [paper]
• [Tucodec Inc] XiangJi Wu, Ziwen Zhang, Jie Feng, Lei Zhou, Junmin Wu: End-to-end Optimized Video Compression with MV-Residual Prediction. CVPR 2020 Workshops. [paper]
• [BUAA] Zhihao Hu, Zhenghao Chen, Dong Xu, Guo Lu, Wanli, Ouyang and Shuhang Gu: Improving Deep Video Compression by Resolution-adaptive Flow Coding. ECCV 2020. [paper]
• [Hikvision] Jianing Deng, LiWang, Shiliang Pu, Cheng Zhuo: Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement. AAAI 2020. [paper]
• [UCSD] Vijay Veerabadran, Reza Pourreza, Amirhossein Habibian, Taco Cohen: Adversarial Distortion for Learned Video Compression. Arxiv. [paper]
• [Nokia] Nannan Zou, Honglei Zhang, Francesco Cricri, Hamed R. Tavakoli, Jani Lainema, Emre Aksu, Miska Hannuksela, Esa Rahtu: End-to-End Learning for Video Frame Compression with Self-Attention. Arxiv. [paper]
• [SJTU] Guo Lu, Chunlei Cai, Xiaoyun Zhang, Li Chen, Wanli Ouyang, Dong Xu, Zhiyong Gao: Content Adaptive and Error Propagation Aware Deep Video Compression. ECCV 2020. [paper]
• [Qualcomm AI] Adam Golinski, Reza Pourreza, Yang Yang, Guillaume Sautiere, Taco S Cohen: Feedback Recurrent Autoencoder for Video Compression. Arxiv. [paper]
• [ETH Zurich] Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte: Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model. Arxiv. [paper]
• [ETH Zurich] Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte: Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement. Arxiv. [paper]
• [IETR] Théo Ladune (IETR), Pierrick Philippe, Wassim Hamidouche (IETR), Lu Zhang (IETR), Olivier Déforges (IETR): ModeNet: Mode Selection Network For Learned Video Coding. Arxiv. [paper]
• [NVIDIA] Ting-Chun Wang, Arun Mallya, Ming-Yu Liu: One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing. Arxiv. [paper]
• [NJU] Ming Lu, Tong Chen, Dandan Ding, Fengqing Zhu, Zhan Ma: Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning. Arxiv. [paper]
• [University of Bristol] Di Ma, Fan Zhang and David R. Bull: CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement. Arxiv. [paper]
• [Uber ATG] Jerry Liu, Shenlong Wang, Wei-Chiu Ma, Meet Shah, Rui Hu, Pranaab Dhawan, Raquel Urtasun: Conditional Entropy Coding for Efficient Video Compression. ECCV 2020. [paper]

• [Macau University] Yumo Zhang, Zhanchuan Cai , Senior Member, IEEE, and Gangqiang Xiong: A New Image Compression Algorithm Based on Non-Uniform Partition and U-System. TMM 2021. [paper]

• [Tohoku University] Shoma Iwai, Tomo Miyazaki, Yoshihiro Sugaya, and Shinichiro Omachi: Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks. ICPR 2021. [paper]

• [Nanjing University] Tong Chen, Haojie Liu, Zhan Ma, Qiu Shen, Xun Cao, and Yao Wang: End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling. TIP 2021. [paper]

• [USTC] Yefei Wang, Dong Liu, Siwei Ma, Feng Wu, Wen Gao: Ensemble Learning-Based Rate-Distortion Optimization for End-to-End Image Compression. TCSVT 2021. [paper]

• [Peking University] Yueyu Hu, Wenhan Yang, Zhan Ma, Jiaying Liu: Learning End-to-End Lossy Image Compression: A Benchmark. TPAMI 2021. [paper]

• [SFU] Mohammad Akbari, Jie Liang, Jingning Han, Chengjie Tu: Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks. TMM 2021. [paper]

• [USTC] Zongyu Guo, Zhizheng Zhang, Runsen Feng and Zhibo Chen: Causal Contextual Prediction for Learned Image Compression. TCSVT 2021. [paper]

• [Sejong University] Khawar Islam, Dang Lien Minh, Sujin Lee, Hyeonjoon Moon: Image Compression with Recurrent Neural Network and Generalized Divisive Normalization. CVPR 2021. [paper]

• [USTC] Haichuan Ma, Dong Liu, Cunhui Dong, Li Li, Feng Wu: End-to-End Image Compression with Probabilistic Decoding. [paper]

• [SenseTime Research] Baocheng Sun, Meng Gu, Dailan He, Tongda Xu, Yan Wang, Hongwei Qin: HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning. [paper]

• [Seoul National University] Myungseo Song, Jinyoung Choi, Bohyung Han: Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform. ICCV 2021. [paper]

• [Northwestern Polytechnical University] Fei Yang, Luis Herranz, Yongmei Cheng, Mikhail G. Mozerov: Slimmable Compressive Autoencoders for Practical Neural Image Compression. CVPR 2021. [paper]

• [SenseTime Research] Dailan He, Yaoyan Zheng, Baocheng Sun, Yan Wang, Hongwei Qin: Checkerboard Context Model for Efficient Learned Image Compression. CVPR 2021. [paper]

• [Peng Cheng Lab] Yuanchao Bai, Xianming Liu, Wangmeng Zuo, Yaowei Wang, Xiangyang Ji: Learning Scalable ℓ∞-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression. CVPR 2021. [paper]

• [SJTU] Xi Zhang, Xiaolin Wu: Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton. CVPR 2021. [paper]

• [HIT] Yang Wang, Xiaopeng Fan, Ruiqin Xiong, Debin Zhao, Wen Gao: Neural Network-based Enhancement to Inter Prediction for Video Coding. TCSVT 2021. [paper]

• [BBC Research] Marc Górriz Blanch, Saverio Blasi, Alan F. Smeaton, Noel E. O’Connor, and Marta Mrak: Neural Network-based Enhancement to Inter Prediction for Video Coding. JSTSP 2021. [paper]

• [Microsoft Research Asia] Jiahao Li, Bin Li, Yan Lu: Deep Contextual Video Compression. [paper]

• [HIT] Hengyu Man, Xiaopeng Fan, Ruiqin Xiong, Debin Zhao: Data Clustering-Driven Neural Network for Intra Prediction. [paper]

• [iSIZE] Aaron Chadha, Yiannis Andreopoulos: Deep Perceptual Preprocessing for Video Coding. CVPR 2021. [paper]

• [Hosei University] Chi D. K. Pham, Chen Fu, Jinjia Zhou: Deep Learning Based Spatial-Temporal In-Loop Filtering for Versatile Video Coding. CVPR 2021. [paper]

• [Qualcomm Technologies] Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Coban, Marta Karczewicz, Yinhao Zhu, Yang Yang, Amir Said, Taco S. Cohen: Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces. [paper]

• [Nanjing University] Ming Lu and Zhan Ma: High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation. [paper]

• [New York University] Jiuhong Xiao, Lavisha Aggarwal, Prithviraj Banerjee, Manoj Aggarwal, and Gerard Medioni: Identity Preserving Loss for Learned Image Compression. [paper]

• [University of Texas] Li-Heng Chen, Christos G. Bampis, Zhi Li, Lukas Krasula, and Alan C. Bovik: Estimating the Resize Parameter in End-to-end Learned Image Compression. [paper]

• [UCAS] Renjie Zou, Chunfeng Song and Zhaoxiang Zhang: The Devil Is in the Details: Window-based Attention for Image Compression. [paper]

• [UESTC] Xiaosu Zhu, Jingkuan Song, Lianli Gao Feng Zheng Heng Tao Shen: Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression. [paper]

• [Technical University of Munich] A. Burakhan Koyuncu, Han Gao, Eckehard Steinbach: contextformer: A Transformer with spatio-channel attention for context modeling in learned image compression. [paper]

• [Peking University] Dezhao Wang Wenhan Yang Yueyu Hu Jiaying Liu: Neural Data-Dependent Transform for Learned Image Compression. [paper]

• [Tsinghua University] Dailan He1, Ziming Yang, Weikun Peng, Rui Ma, Hongwei Qin, Yan Wang: ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding. [paper]

• [Peking University] Yi Ma, Yongqi Zhai, and Ronggang Wang: DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression. [paper]

• [Friedrich-Alexander University] Fabian Brand, Kristian Fischer, Alexander Kopte, and Andre Kaup: Learning True Rate-Distortion-Optimization for End-To-End Image Compression. [paper]

• [Alibaba Group] Yichen Qian, Ming Lin, Xiuyu Sun: EnTroformer: A Transformer-based Entropy Model for Learned Image Compression. [paper]

• [SenseTime Research] Dailan He, Ziming Yang, Yuan Chen, Qi Zhang, Hongwei Qin, Yan Wang: Post-Training Quantization for Cross-Platform Learned Image Compression. [paper]

• [Vrije Universiteit Amsterdam] Yura Perugachi-Diaz, Guillaume Sautiere, Davide Abati, Yang Yang: Region-of-Interest Based Neural Video Compression. [paper]

• [Nanjing University of Aeronautics and Astronautics] Haoyue Tian, Pan Gao, Ran Wei, Manoranjan Paul: Dilated Convolutional Neural Network-based Deep Reference Picture Generation for video compression. [paper]

• [Koç University] M. Akın Yılmaz, and A. Murat Tekalp: End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression. TIP 2022. [paper]

• [Nanjing University] Dandan Ding , Xiang Gao, Chenran Tang, and Zhan Ma: Neural Reference Synthesis for Inter Frame Coding. TIP 2022. [paper]