Awesome Neural Compression Resources

Recent papers and codes related to learning-based data compression, including images, videos, audios, point clouds, nerf models, Gaussian Splatting.

2024

CVPR

• Xinjie Zhang, Ren Yang, Dailan He, Xingtong Ge, Tongda Xu, Yan Wang, Hongwei Qin, Jun Zhang, Boosting Neural Representations for Videos with a Conditional Decoder. [paper][code]
• Xingtong Ge, Jixiang Luo, Xinjie Zhang, Tongda Xu, Guo Lu, Dailan He, Jing Geng, Yan Wang, Jun Zhang, Hongwei Qin, Task-Aware Encoder Control for Deep Video Compression. [paper]
• Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont, C3: High-performance and low-complexity neural compression from a single image or video. [paper]
• Zhihao Duan, Ming Lu, Justin Yang, Jiangpeng He, Zhan Ma, Fengqing Zhu, Towards Backward-Compatible Continual Learning of Image Compression. [paper][code]
• Hao Yan, Zhihui Ke, Xiaobo Zhou, Tie Qiu, Xidong Shi, Dadong Jiang, DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes. [paper][code]
• Jiahao Li, Bin Li, Yan Lu, Neural Video Compression with Feature Modulation. [paper][code]
• Yuheng Jiang, Zhehao Shen, Penghao Wang, Zhuo Su, Yu Hong, Yingliang Zhang, Jingyi Yu, Lan Xu, HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting. [paper]
• Simon Niedermayr, Josef Stumpfegger, Rüdiger Westermann, Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis. [paper][code]
• Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta, Nasser M. Nasrabadi, Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis. [paper]
• Sicheng Li, Hao Li, Yiyi Liao, Lu Yu, NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-efficient Scene Representation. [paper]
• **, Combining Frame and GOP Embeddings for Neural Video Representation.
• **, Learned Lossless Image Compression based on Bit Plane Slicing.
• **, Look-Up Table Compression for Efficient Image Restoration.
• **, Implicit Motion Function.
• **, Generative Latent Coding for Ultra-Low Bitrate Image Compression.
• **, Versatile Neural Video Codec.

ECCV

NeurIPS

ICLR

• Tongda Xu, Ziran Zhu, Dailan He, Yanghao Li, Lina Guo, Yuanyuan Wang, Zhe Wang, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang, Idempotence and Perceptual Image Compression. [paper][code]
• Yufeng Zhang, Hang Yu, Jianguo Li, Weiyao Lin, Finite-State Autoregressive Entropy Coding for Efficient Learned Lossless Compression. [paper]
• Joo Chan Lee, Daniel Rho, Seungtae Nam, Jong Hwan Ko, Eunbyung Park, Coordinate-Aware Modulation for Neural Fields. [paper][code]
• Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, Marcus Hutter, Joel Veness, Language Modeling Is Compression. [paper]
• Yiwei Zhang, Guo Lu, Yunuo Chen, Shen Wang, Yibo Shi, Jing Wang, Li Song, Neural Rate Control for Learned Video Compression. [paper]
• Guangchi Fang, Qingyong Hu, Longguang Wang, Yulan Guo, ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression. [paper]
• Marlène Careil, Matthew J. Muckley, Jakob Verbeek, Stéphane Lathuilière, Towards image compression with perfect realism at ultra-low bitrates. [paper]
• Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato, RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. [paper][code]
• Ivan Butakov, Alexander Tolmachev, Sofia Malanchuk, Anna Neopryatnaya, Alexey Frolov, Kirill Andreev, Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression. [paper]
• Han Li, Shaohui Li, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, FTIC: Frequency-Aware Transformer for Learned Image Compression. [paper]
• Edouard Yvinec, Arnaud Dapogny, Kevin Bailly, Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings. [paper]
• Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend, Entropy Coding of Unordered Data Structures. [paper][code]

ICML

AAAI

• Ming Lu, Zhihao Duan, Fengqing Zhu, Zhan Ma, Deep Hierarchical Video Compression. [paper]
• Huiming Zheng, Wei Gao, End-to-End RGB-D Image Compression via Exploiting Channel-Modality Redundancy. [paper]
• Ao Luo, Linxin Song, Keisuke Nonaka, Kyohei Unno, Heming Sun, Masayuki Goto, Jiro Kattok, SCP: Spherical-Coordinate-Based Learned Point Cloud Compression. [paper][code]
• Shilv Cai, Liqun Chen, Sheng Zhong, Luxin Yan, Jiahuan Zhou, Xu Zou, Make Lossy Compression Meaningful for Low-Light Image. [paper]
• Qiuyu Duan, Zhongyun Hua, Qing Liao, Yushu Zhang, LEO Yu Zhang, Conditional Backdoor Attack via JPEG Compression. [paper]
• Guangchi Fang, Qingyong Hu, Longguang Wang, Yulan Guo, ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression. [paper]
• Chuanbo Tang, Xihua Sheng, Zhuoyuan Li, Haotian Zhang, Li Li, Dong Liu, Offline and Online Optical Flow Enhancement for Deep Video Compression. [paper]
• Miaohui Wang, Runnan Huang, Hengjin Dong, Di Lin, Song Yun, Wuyuan Xie, msLPCC: A Multimodal-Driven Scalable Framework for Deep LiDAR Point Cloud Compression. [paper]

IJCAI

2023

CVPR

• Jinming Liu, Heming Sun, Jiro Katto, Learned Image Compression with Mixed Transformer-CNN Architectures. [paper][code]
• Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim, Context-Based Trit-Plane Coding for Progressive Image Compression. [paper] [code]
• Eirikur Agustsson, David Minnen, George Toderici, Fabian Mentzer, Multi-Realism Image Compression with a Conditional Generator. [paper]
• Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng, Jinli Suo, Qionghai Dai, TINC: Tree-structured Implicit Neural Compression. [paper][code]
• Xi Zhang, Xiaolin Wu, LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression. [paper]
• Yi Yu, Yufei Wang, Wenhan Yang, Shijian Lu, Yap-peng Tan, Alex C. Kot, Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger. [paper]
• Juncheol Ye, Hyunho Yeo, Jinwoo Park, Dongsu Han, AccelIR: Task-aware Image Compression for Accelerating Neural Restoration. [paper][code]
• Jiahao Li, Bin Li, Yan Lu, Neural Video Compression with Diverse Contexts. [paper][code]
• Linfeng Qi, Jiahao Li, Bin Li, Houqiang Li, Yan Lu, Motion Information Propagation for Neural Video Compression. [paper]
• Zhihao Hu, Dong Xu, Complexity-guided Slimmable Decoder for Efficient Deep Video Compression. [paper]
• Bowen Liu, Yu Chen, Rakesh Chowdary Machineni, Shiyu Liu, Hun-Seok Kim, MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding. [paper][code]
• David Alexandre, Hsueh-Ming Hang, Wen-Hsiao Peng, Hierarchical B-frame Video Compression Using Two-Layer CANF without Motion Coding. [paper][code]
• Carlos Gomes, Roberto Azevedo, Christopher Schroers, Video Compression with Entropy-Constrained Neural Representations.[paper]
• Hao Chen, Matt Gwilliam, Ser-Nam Lim, Abhinav Shrivastava, HNeRV: A Hybrid Neural Representation for Videos. [paper][code]
• 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. [paper][code]
• Shishira R Maiya, Sharath Girish, Max Ehrlich, Hanyu Wang, Kwot Sin Lee, Patrick Poirson, Pengxiang Wu, Chen Wang, Abhinav Shrivastava, NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling. [paper]
• Qi Zhao, M. Salman Asif, Zhan Ma, DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos. [paper]
• Liao Wang, Qiang Hu, Qihan He, Ziyu Wang, Jingyi Yu, Tinne Tuytelaars, Lan Xu, Minye Wu, Neural Residual Radiance Fields for Streamably FreeViewpoint Videos. [paper]
• Rui Song, Chunyang Fu, Shan Liu, Ge Li, Efficient Hierarchical Entropy Model for Learned Point Cloud Compression. [paper]

ICCV

• Yibo Yang, Stephan Mandt, Computationally-Efficient Neural Image Compression with Shallow Decoders. [paper][code]
• Yuan Tian, Guo Lu, Guangtao Zhai, Zhiyong Gao, Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic Compression. [paper]
• Jongmin Park, Jooyoung Lee, Munchurl Kim, COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability. [paper] [code]
• Sheng Shen, Huanjing Yue, Jingyu Yang, Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression. [paper]
• Lv Tang, Xinfeng Zhang, Gai Zhang, Xiaoqi Ma, Scene Matters: Model-based Deep Video Compression. [paper]
• Mengyao Li, Liquan Shen, Peng Ye, Guorui Feng, Zheyin Wang, RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionary. [paper] [code]
• Sharath Girish, Abhinav Shrivastava, Kamal Gupta, SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations. [paper] [code]
• 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. [paper] [code]
• Lvfang Tao, Wei Gao, Ge Li, Chenhao Zhang, AdaNIC: Towards Practical Neural Image Compression via Dynamic Transform Routing. [paper]
• Ruoyu Feng, Yixin Gao, Xin Jin, Runsen Feng, Zhibo Chen, Semantically Structured Image Compression via Irregular Group-Based Decoupling. [paper]

NeurIPS

• Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato, Compression with Bayesian Implicit Neural Representations. [paper][code]
• Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, Kundan Kumar, High-Fidelity Audio Compression with Improved RVQGAN. [paper][code]
• Yanghao Li, Tongda Xu, Yan Wang, Jingjing Liu, Ya-Qin Zhang, Idempotent Learned Image Compression with Right-Inverse. [paper]
• Gergely Flamich, Stratis Markou, Jose Miguel Hernandez Lobato, Faster Relative Entropy Coding with Greedy Rejection Coding. [paper]
• Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, David Bull, HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation. [paper][code]
• Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura, Neural Image Compression: Generalization, Robustness, and Spectral Biases. [paper][code]
• Ruihan Yang, Stephan Mandt, Lossy Image Compression with Conditional Diffusion Models. [paper]
• Muhammad Salman Ali, Yeongwoong Kim, Maryam Qamar, Sung-Chang Lim, Donghyun Kim, Chaoning Zhang, Sung-Ho Bae, Hui Yong Kim, Towards Efficient Image Compression Without Autoregressive Models. [paper]
• Sadaf Salehkalaibar, Buu Phan, Jun Chen, Wei Yu, Ashish Khisti, On the choice of Perception Loss Function for Learned Video Compression. [paper]
• Po-han Li, Sravan Kumar Ankireddy, Ruihan Zhao, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari, Ufuk Topcu, Sandeep Chinchali, Hyeji Kim, Task-aware Distributed Source Coding under Dynamic Bandwidth. [paper][code]
• Haoyu Guo, Sida Peng, Yunzhi Yan, Linzhan Mou, Yujun Shen, Hujun Bao, Xiaowei Zhou, Compact Neural Volumetric Video Representations with Dynamic Codebooks. [paper][code]

ICLR

• Xinjie Zhang, Jiawei Shao, Jun Zhang, LDMIC: Learning-based Distributed Multi-view Image Coding. [paper][code]
• Jinxi Xiang, Kuan Tian, Jun Zhang, MIMT: Masked Image Modeling Transformer for Video Compression. [paper]
• Langwen Huang, Torsten Hoefler, Compressing multidimensional weather and climate data into neural networks. [paper]
• Wang Guo-Hua, Jiahao Li, Bin Li, Yan Lu, EVC: Towards Real-Time Neural Image Compression with Mask Decay. [paper][code]

ICML

• Tongda Xu, Han Gao, Chenjian Gao, Yuanyuan Wang, Dailan He, Jinyong Pi, Jixiang Luo, Ziyu Zhu, Mao Ye, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang, Bit Allocation using Optimization. [paper][code]
• Matthew J. Muckley, Alaaeldin El-Nouby, Karen Ullrich, Hervé Jégou, Jakob Verbeek, Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. [paper]
• Hee Min Choi, Hyoa Kang, Dokwan Oh, Is Overfitting Necessary for Implicit Video Representation?[paper]

AAAI

• Yujun Huang, Bin Chen, Shiyu Qin, Jiawei Li, Yaowei Wang, Tao Dai, Shu-Tao Xia, Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain. [paper]
• Mingyue Cui, Junhua Long, Mingjian Feng, Boyang Li, Huang Kai, OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression with Local Enhancement. [paper][code]
• Xuhao Jiang, Weimin Tan, Tian Tan, Bo Yan, Liquan Shen, Multi-Modality Deep Network for Extreme Learned Image Compression. [paper]
• Xinjian Zhang, Su Yang, Wuyang Luo, Longwen Gao, Weishan Zhang, Video Compression Artifact Reduction by Fusing Motion Compensation and Global Context in a Swin-CNN Based Parallel Architecture. [paper] [code]
• Yujun Huang, Bin Chen, Shiyu Qin, Jiawei Li, Yaowei Wang, Tao Dai, Shu-Tao Xia, TinyNeRF: Towards 100 x Compression of Voxel Radiance Fields. [paper][code]
• Qishi Dong, Fengwei Zhou, Ning Kang, Chuanlong Xie, Shifeng Zhang, Jiawei Li, Heng Peng, Zhenguo Li, DAMix: Exploiting Deep Autoregressive Model Zoo for Improving Lossless Compression Generalization. [paper]
• Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng, Qianni Cao, Jinyuan Qu, Jinli Suo, Qionghai Dai, SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data. [paper] [code]
• Jinhai Yang, Mengxi Guo, Shijie Zhao, Junlin Li, Li Zhang, Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling. [paper] [code]

2022

CVPR

• Tom Ryder, Chen Zhang, Ning Kang, Shifeng Zhang, Split Hierarchical Variational Compression. [paper]
• Zhihao Hu, Guo Lu, Jinyang Guo, Shan Liu, Wei Jiang, Dong Xu, Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction. [paper]
• Jun-Hyuk Kim, Byeongho Heo, Jong-Seok Lee, Joint Global and Local Hierarchical Priors for Learned Image Compression. [paper][code]
• Hochang Rhee, Yeong Il Jang, Seyun Kim, Nam Ik Cho, LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network. [paper]
• Jae-Han Lee, Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim, DPICT: Deep Progressive Image Compression Using Trit-Planes. [paper][code]
• Lina Guo, Xinjie Shi, Dailan He, Yuanyuan Wang, Rui Ma, Hongwei Qin, Yan Wang, Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain. [paper]
• Xiaosu Zhu, Jingkuan Song, Lianli Gao, Feng Zheng, Heng Tao Shen, Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression. [paper][code]
• Dailan He, Ziming Yang, Weikun Peng, Rui Ma, Hongwei Qin, Yan Wang, ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding. [paper]
• Renjie Zou, Chunfeng Song, Zhaoxiang Zhang, The Devil Is in the Details: Window-based Attention for Image Compression. [paper][code]
• Dezhao Wang, Wenhan Yang, Yueyu Hu, Jiaying Liu, Neural Data-Dependent Transform for Learned Image Compression. [paper][code]
• Ning Kang, Shanzhao Qiu, Shifeng Zhang, Zhenguo Li, Shu-Tao Xia, PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework. [paper]
• Zhenghao Chen, Guo Lu, Zhihao Hu, Shan Liu, Wei Jiang, Dong Xu, LSVC: A Learning-based Stereo Video Compression Framework. [paper]
• Jianjun Lei, Xiangrui Liu, Bo Peng, Dengchao Jin, Wanqing Li, Jingxiao Gu, Deep Stereo Image Compression via Bi-directional Coding. [paper]
• Matthias Wödlinger, Jan Kotera, Jan Xu, Robert Sablatnig, SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention. [paper][code]
• Xuanyu Zhou, Charles R. Qi, Yin Zhou, Dragomir Anguelov, RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding. [paper]
• Yun He, Xinlin Ren, Danhang Tang, Yinda Zhang, Xiangyang Xue, Yanwei Fu, Density-preserving Deep Point Cloud Compression. [paper] [project]
• Guangchi Fang, Qingyong Hu, Hanyun Wang, Yiling Xu, Yulan Guo, 3DAC: Learning Attribute Compression for Point Clouds. [paper][code]
• Guo Lu, Tianxiong Zhong, Jing Geng, Qiang Hu, Dong Xu, Learning based Multi-modality Image and Video Compression. [paper]

ECCV

• Yannick Strümpler, Janis Postels, Ren Yang, Luc van Gool, Federico Tombari, Implicit Neural Representations for Image Compression. [paper]
• Joaquim Campos, Simon Meierhans, Abdelaziz Djelouah, Christopher Schroers, Content Adaptive Latents and Decoder for Neural Image Compression. [paper]
• A. Burakhan Koyuncu, Han Gao, Atanas Boev, Georgii Gaikov, Elena Alshina, Eckehard Steinbach, Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression. [paper]
• Chajin Shin, Hyeongmin Lee, Hanbin Son, Sangjin Lee, Dogyoon Lee, Sangyoun Lee, Expanded Adaptive Scaling Normalization for End to End Image Compression. [paper][code]
• Tianyi Liu, Sen He, Vinodh Kumaran Jayakumar, Wei Wang, A Cloud 3D Dataset and Application-Specific Learned Image Compression in Cloud 3D. [paper] [code]
• Meng Li, Shangyin Gao, Yihui Feng, Yibo Shi, Jing Wang, Content-Oriented Learned Image Compression. [paper] [code]
• Yibo Shi, Yunying Ge, Jing Wang, Jue Mao, AlphaVC: High-Performance and Efficient Learned Video Compression. [paper]
• Yung-Han Ho, Chih-Peng Chang, Peng-Yu Chen, Alessandro Gnutti, Wen-Hsiao Peng, CANF-VC: Conditional Augmented Normalizing Flows for Video Compression. [paper][code]
• Fabian Mentzer, Eirikur Agustsson, Johannes Ballé, David Minnen, Nick Johnston, George Toderici, Neural Video Compression using GANs for Detail Synthesis and Propagation. [paper]
• Zhili Chen, Zian Qian, Sukai Wang, Qifeng Chen, Point Cloud Compression with Sibling Context and Surface Priors. [paper] [code]
• Sukai Wang, Ming Liu, Point Cloud Compression using Range Image-based Entropy Model for Autonomous Driving. [paper]
• Ruoyu Feng et al, Image Coding for Machines with Omnipotent Feature Learning. [paper]
• Ka Leong Cheng, Yueqi Xie, Qifeng Chen, Optimizing Image Compression via Joint Learning with Denoising. [paper] [code]
• Zizhang Li, Mengmeng Wang, Huaijin Pi, Kechun Xu, Jianbiao Mei, Yong Liu, E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context. [paper] [code]

NeurIPS

• Anastasia Antsiferova, Sergey Lavrushkin, Maksim Smirnov, Aleksandr Gushchin, Dmitriy Vatolin, Dmitriy Kulikov, Video compression dataset and benchmark of learning-based video-quality metrics. [paper]
• Fabian Mentzer, George Toderici, David Minnen, Sung-Jin Hwang, Sergi Caelles, Mario Lucic, Eirikur Agustsson, VCT: A Video Compression Transformer. [paper][code]
• Tongda Xu, Yan Wang, Dailan He, Chenjian Gao, Han Gao, Kunzan Liu, Hongwei Qin, Multiple-sample Neural Image Compression. [paper]
• Jooyoung Lee, Seyoon Jeong, Munchurl Kim, Selective compression learning of latent representations for variable-rate image compression. [paper][code]
• Chenjian Gao, Tongda Xu, Dailan He, Hongwei Qin, Yan Wang, Flexible Neural Image Compression via Code Editing. [paper]

ICLR

• Anji Liu, Stephan Mandt, Guy Van den Broeck, Lossless Compression with Probabilistic Circuits. [paper]
• Huan Liu, George Zhang, Jun Chen, Ashish J Khisti, Lossy Compression with Distribution Shift as Entropy Constrained Optimal Transport. [paper]
• Yichen Qian, Ming Lin, Xiuyu Sun, Zhiyu Tan, Rong Jin, Entroformer: A Transformer-based Entropy Model for Learned Image Compression. [paper][code]
• Yibo Yang, Stephan Mandt, Towards Empirical Sandwich Bounds on the Rate-Distortion Function. [paper]
• Yinhao Zhu, Yang Yang, Taco Cohen, Transformer-based Transform Coding. [paper]
• Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans, Autoregressive Diffusion Models. [paper]

ICML

• Zeyu Yan, Fei Wen, Peilin Liu, Optimally Controllable Perceptual Lossy Compression. [paper]
• Rui Shu, Stefano Ermon, Bit Prioritization in Variational Autoencoders via Progressive Coding. [paper]
• Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato, Fast Relative Entropy Coding with A* coding. [paper]
• Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang, Fast Lossless Neural Compression with Integer-Only Discrete Flows. [paper]

AAAI

• Yuanchao Bai, Xu Yang, Xianming Liu, Junjun Jiang, Yaowei Wang, Xiangyang Ji, Wen Gao, Towards End-to-End Image Compression and Analysis with Transformers. [paper]
• Fangdong Chen, Yumeng Xu, Li Wang, Two-Stage Octave Residual Network for End-to-End Image Compression. [paper]
• Linfeng Cao, Aofan Jiang, Wei Li, Huaying Wu, Nanyang Ye, OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression. [paper] [code]
• Chunyang Fu, Ge Li, Rui Song, Wei Gao, Shan Liu, OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression. [paper][code]

IJCAI

• Ren Yang, Radu Timofte, Luc Van Gool, Perceptual Learned Video Compression with Recurrent Conditional GAN. [paper]
• Tingyu Fan, Linyao Gao, Yiling Xu, Zhu Li, Dong Wang, D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction. [paper][code]

2021

CVPR

• Zhihao Hu, Guo Lu, Dong Xu, FVC: A New Framework towards Deep Video Compression in Feature Space. [paper]
• Bowen Liu, Yu Chen, Shiyu Liu, Hun-Seok Kim, Deep Learning in Latent Space for Video Prediction and Compression. [paper][code]
• Aaron Chadha, Yiannis Andreopoulos, Deep Perceptual Preprocessing for Video Coding. [paper]
• Shifeng Zhang, Chen Zhang, Ning Kang, Zhenguo Li, iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression. [paper]
• Jan P. Klopp, Keng-Chi Liu, Liang-Gee Chen, Shao-Yi Chien, How to Exploit the Transferability of Learned Image Compression to Conventional Codecs. [paper]
• Xi Zhang, Xiaolin Wu, Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton. [paper]
• Dailan He, Yaoyan Zheng, Baocheng Sun, Yan Wang, Hongwei Qin, Checkerboard Context Model for Efficient Learned Image Compression. [paper][code]
• Fei Yang, Luis Herranz, Yongmei Cheng, Mikhail G. Mozerov, Slimmable Compressive Autoencoders for Practical Neural Image Compression. [paper]
• Yuanchao Bai, Xianming Liu, Wangmeng Zuo, Yaowei Wang, Xiangyang Ji, Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression. [paper]
• Ze Cui, Jing Wang, Shangyin Gao, Tiansheng Guo, Yihui Feng, Bo Bai, Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation. [paper][code]
• Yuval Bahat, Tomer Michaeli, What's in the Image? Explorable Decoding of Compressed Images. [paper]
• Xin Deng, Wenzhe Yang, Ren Yang, Mai Xu, Enpeng Liu, Qianhan Feng, Radu Timofte, Deep Homography for Efficient Stereo Image Compression. [paper][code]
• Zizheng Que, Guo Lu, Dong Xu, VoxelContext-Net: An Octree Based Framework for Point Cloud Compression. [paper]
• Chi D. K. Pham, Chen Fu, Jinjia Zhou, Deep Learning Based Spatial-Temporal In-Loop Filtering for Versatile Video Coding. [workshop]

ICCV

• Jan P. Klopp, Keng-Chi Liu, Shao-Yi Chien, Liang-Gee Chen, Online-Trained Upsampler for Deep Low Complexity Video Compression. [paper]
• Reza Pourreza, Taco S Cohen, Extending Neural P-Frame Codecs for B-Frame Coding. [paper]
• Mehrdad Khani, Vibhaalakshmi Sivaraman, Mohammad Alizadeh, Efficient Video Compression via Content-Adaptive Super-Resolution. [paper][code]
• Oren Rippel, Alexander G. Anderson, Kedar Tatwawadi, Sanjay Nair, Craig Lytle, Lubomir Bourdev, ELF-VC: Efficient Learned Flexible-Rate Video Coding. [paper][project]
• Xueyang Fu, Xi Wang, Aiping Liu, Junwei Han, Zheng-Jun Zha, Learning Dual Priors for JPEG Compression Artifacts Removal. [paper]
• Myungseo Song, Jinyoung Choi, Bohyung Han, Variable-Rate Deep Image Compression Through Spatially-Adaptive Feature Transform. [paper][code]
• Ge Gao, Pei You, Rong Pan, Shunyuan Han, Yuanyuan Zhang, Yuchao Dai, Hojae Lee, Neural Image Compression via Attentional Multi-Scale Back Projection and Frequency Decomposition. [paper]

NeurIPS

• Jiahao Li, Bin Li, Yan Lu, Deep Contextual Video Compression. [paper]
• Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li, iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder. [paper]
• George Zhang, Jingjing Qian, Jun Chen, Ashish Khisti, Universal Rate-Distortion-Perception Representations for Lossy Compression. [paper]
• Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison, Lossy Compression for Lossless Prediction. [paper]
• Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li, OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression. [paper]
• Siddharth Reddy, Anca D. Dragan, Sergey Levine, Pragmatic Image Compression for Human-in-the-Loop Decision-Making. [paper]
• Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava, NeRV: Neural Representations for Videos. [paper][code]

ICLR

• Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt, Hierarchical Autoregressive Modeling for Neural Video Compression. [paper][code]
• Ties van Rozendaal, Iris A.M. Huijben, Taco S. Cohen, Overfitting for Fun and Profit: Instance-Adaptive Data Compression . [paper]
• Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon, Improved Autoregressive Modeling with Distribution Smoothing. [paper]
• Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans, IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression. [paper]
• Yichen Qian, Zhiyu Tan, Xiuyu Sun, Ming Lin, Dongyang Li, Zhenhong Sun, Hao Li, Rong Jin, Learning Accurate Entropy Model with Global Reference for Image Compression. [paper][code]

ICML

• Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen, Soft then Hard: Rethinking the Quantization in Neural Image Compression. [paper]
• Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison, Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. [paper]
• Zeyu Yan, Fei Wen, Rendong Ying, Chao Ma, Peilin Liu, On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework. [paper]
• Ruihan Yang, Yibo Yang, Joseph Marino, Yang Yang, and Stephan Mandt, Deep generative video compression with temporal autoregressive transforms. [workshop]

AAAI

• Mohammad Akbari, Jie Liang, Jingning Han, Chengjie Tu, Learned Bi-Resolution Image Coding Using Generalized Octave Convolutions. [paper]

2020

CVPR

• Jianping Lin, Dong Liu, Houqiang Li, Feng Wu, M-LVC: Multiple Frames Prediction for Learned Video Compression. [paper][code]
• Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte, Learning for Video Compression With Hierarchical Quality and Recurrent Enhancement. [paper][code]
• Eirikur Agustsson, David Minnen, Nick Johnston, Johannes Balle, Sung Jin Hwang, George Toderici, Scale-Space Flow for End-to-End Optimized Video Compression. [paper]
• Rongjie Liu, Meng Li, Li Ma, Efficient in-situ image and video compression through probabilistic image representation. [paper]
• Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto, Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules. [paper]
• Fabian Mentzer, Luc Van Gool, Michael Tschannen, Learning Better Lossless Compression Using Lossy Compression. [paper]
• Chaoyi Lin, Jiabao Yao, Fangdong Chen, Li Wang, A Spatial RNN Codec for End-to-End Image Compression. [paper]
• Innfarn Yoo, Xiyang Luo, Yilin Wang, Feng Yang, Peyman Milanfar, GIFnets: Differentiable GIF Encoding Framework. [paper]
• Danhang Tang et. al, Deep Implicit Volume Compression. [paper]
• Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, Raquel Urtasun, OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression. [paper]

ECCV

• Zhihao Hu, Zhenghao Chen, Dong Xu, Guo Lu, Wanli Ouyang, Shuhang Gu, Improving Deep Video Compression by Resolution-adaptive Flow Coding. [paper]
• Guo Lu, Chunlei Cai, Xiaoyun Zhang, Li Chen, Wanli Ouyang, Dong Xu, Zhiyong Gao, Content Adaptive and Error Propagation Aware Deep Video Compression. [paper]
• Wenyu Sun, Chen Tang, Weigui Li, Zhuqing Yuan, Huazhong Yang, Yongpan Liu, High-quality Single-model Deep Video Compression with Frame-Conv3D and Multi-frame Differential Modulation. [paper]
• Jerry Liu, Shenlong Wang, Wei-Chiu Ma, Meet Shah, Rui Hu, Pranaab Dhawan, Raquel Urtasun, Conditional Entropy Coding for Efficient Video Compression. [paper]
• Sharon Ayzik, Shai Avidan, Deep Image Compression using Decoder Side Information. [paper] [code]
• Jinyoung Choi, Bohyung Han, Task-Aware Quantization Network for JPEG Image Compression. [paper]

NeurIPS

• Yibo Yang, Robert Bamler, Stephan Mandt, Improving Inference for Neural Image Compression. [paper]
• Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson, High-Fidelity Generative Image Compression. [paper][code]
• Eirikur Agustsson, Lucas Theis, Universally Quantized Neural Compression. [paper]
• Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato, Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. [paper]
• Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun, MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models. [paper]

ICLR

• James Townsend, Thomas Bird, Julius Kunze, David Barber, HiLLoC: lossless image compression with hierarchical latent variable models . [paper]

ICML

• Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse, Evaluating Lossy Compression Rates of Deep Generative Models. [paper]

AAAI

• Haojie Liu, Han shen, Lichao Huang, Ming Lu, Tong Chen, Zhan Ma, Learned Video Compression via Joint Spatial-Temporal Correlation Exploration. [paper]
• Yueyu Hu, Wenhan Yang, Jiaying Liu, Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression. [paper]

IJCAI

• Zhisheng Zhong, Hiroaki Akutsu, Kiyoharu Aizawa, Channel-Level Variable Quantization Network for Deep Image Compression. [paper]
• Menglu Wang, Xueyang Fu, Zepei Sun, Zheng-Jun Zha, JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks. [paper]

2019

CVPR

• Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto, Learning Image and Video Compression Through Spatial-Temporal Energy Compaction. [paper]
• Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, Zhiyong Gao, DVC: An End-To-End Deep Video Compression Framework. [paper]
• Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool, Practical Full Resolution Learned Lossless Image Compression. [paper]
• Zihao Liu, Xiaowei Xu, Tao Liu, Qi Liu, Yanzhi Wang, Yiyu Shi, Wujie Wen, Meiping Huang, Haiyun Yuan, Jian Zhuang, Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds. [paper]

ICCV

• Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander G. Anderson, Lubomir Bourdev, Learned Video Compression. [paper]
• Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool, Generative Adversarial Networks for Extreme Learned Image Compression. [paper][code]
• Jerry Liu, Shenlong Wang, Raquel Urtasun, DSIC: Deep Stereo Image Compression. [paper]
• Yoojin Choi, Mostafa El-Khamy, Jungwon Lee, Variable Rate Deep Image Compression With a Conditional Autoencoder. [paper]
• Xueyang Fu, Zheng-Jun Zha, Feng Wu, Xinghao Ding, John Paisley, JPEG Artifacts Reduction via Deep Convolutional Sparse Coding. [paper]

NeurIPS

• Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt, Deep Generative Video Compression. [paper]
• Emiel Hoogeboom, Jorn W.T. Peters, Rianne van den Berg, Max Welling, Integer Discrete Flows and Lossless Compression. [paper]
• Jonathan Ho, Evan Lohn, Pieter Abbeel, Compression with Flows via Local Bits-Back Coding. [paper]

ICLR

• Johannes Ballé, Nick Johnston, David Minnen, Integer Networks for Data Compression with Latent-Variable Models. [paper][code] [code]
• James Townsend, Thomas Bird, David Barber, Practical lossless compression with latent variables using bits back coding . [paper][code]
• Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack, Context-adaptive Entropy Model for End-to-end Optimized Image Compression. [paper][code]

ICML

• Yochai Blau, Tomer Michaeli, Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff. [paper]
• Friso H. Kingma, Pieter Abbeel, Jonathan Ho, Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables. [paper][code]
• Yan Wu, Mihaela Rosca, Timothy Lillicrap, Deep Compressed Sensing. [paper]
• Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon, Neural Joint Source-Channel Coding. [paper][code]

AAAI

• Xichuan Zhou, Lang Xu, Shujun Liu, Yingcheng Lin, Lei Zhang, Cheng Zhuo, An Efficient Compressive Convolutional Network for Unified Object Detection and Image Compression. [paper]