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Awesome-Neural-Compression

★ 83 Open GitHub ↗

Curated list of papers and resources focused on neural compression, intended to keep pace with the anticipated surge of research in the recent years.

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# 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]](https://arxiv.org/abs/2402.18152)[[code]](https://github.com/Xinjie-Q/Boosting-NeRV)
* *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]](https://arxiv.org/abs/2404.04848)
* *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]](https://arxiv.org/abs/2312.02753)
* *Zhihao Duan, Ming Lu, Justin Yang, Jiangpeng He, Zhan Ma, Fengqing Zhu*, Towards Backward-Compatible Continual Learning of Image Compression. [[paper]](https://arxiv.org/abs/2402.18862)[[code]](https://gitlab.com/viper-purdue/continual-compression)
* *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]](https://haoyan14.github.io/DS-NeRV/)
* *Jiahao Li, Bin Li, Yan Lu*, Neural Video Compression with Feature Modulation. [[paper]](https://arxiv.org/abs/2402.17414)[[code]](https://github.com/microsoft/DCVC)
* *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]](https://arxiv.org/abs/2312.03461)
* *Simon Niedermayr, Josef Stumpfegger, Rüdiger Westermann*, Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis. [[paper]](https://arxiv.org/abs/2401.02436)[[code]](https://github.com/KeKsBoTer/c3dgs/)
* *Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta, Nasser M. Nasrabadi*, Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis. [[paper]](https://arxiv.org/abs/2403.16258)
* *Sicheng Li, Hao Li, Yiyi Liao, Lu Yu*, NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-efficient Scene Representation. [[paper]](https://arxiv.org/abs/2404.02185)
* *Jens Eirik Saethre, Roberto Azevedo, Christopher Schroers*, Combining Frame and GOP Embeddings for Neural Video Representation. [[paper]](https://studios.disneyresearch.com/2024/06/17/combining-frame-and-gop-embeddings-for-neural-video-representation/)
* *Zhe Zhang, Huairui Wang, Zhenzhong Chen, Shan Liu*, Learned Lossless Image Compression based on Bit Plane Slicing. [[paper]](https://openaccess.thecvf.com//content/CVPR2024/papers/Zhang_Learned_Lossless_Image_Compression_based_on_Bit_Plane_Slicing_CVPR_2024_paper.pdf)[[code]](https://github.com/zz022/arib-bps)
* *Zhaoyang Jia, Jiahao Li, Bin Li, Houqiang Li, Yan Lu*, Generative Latent Coding for Ultra-Low Bitrate Image Compression. [[paper]](https://openaccess.thecvf.com/content/CVPR2024/papers/Jia_Generative_Latent_Coding_for_Ultra-Low_Bitrate_Image_Compression_CVPR_2024_paper.pdf)

### ECCV
* *Xinjie Zhang, Xingtong Ge, Tongda Xu, Dailan He, Yan Wang, Hongwei Qin, Guo Lu, Jing Geng, Jun Zhang*, GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting. [[paper]](https://arxiv.org/abs/2402.18152)[[code]](https://github.com/Xinjie-Q/GaussianImage)
* *Zhening Liu, Xinjie Zhang, Jiawei Shao, Zehong Lin, Jun Zhang*, Bidirectional Stereo Image Compression with Cross-Dimensional Entropy Model. [[paper]](https://arxiv.org/abs/2407.10632)[[code]](https://github.com/LIUZhening111/BiSIC)

### 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]](https://arxiv.org/abs/2401.08920)[[code]](https://github.com/tongdaxu/Idempotence-and-Perceptual-Image-Compression)
* *Yufeng Zhang, Hang Yu, Jianguo Li, Weiyao Lin*, Finite-State Autoregressive Entropy Coding for Efficient Learned Lossless Compression. [[paper]](https://openreview.net/forum?id=D5mJSNtUtv)
* *Joo Chan Lee, Daniel Rho, Seungtae Nam, Jong Hwan Ko, Eunbyung Park*, Coordinate-Aware Modulation for Neural Fields. [[paper]](https://arxiv.org/abs/2311.14993)[[code]](https://maincold2.github.io/cam/)
* *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]](https://arxiv.org/abs/2309.10668)
* *Yiwei Zhang, Guo Lu, Yunuo Chen, Shen Wang, Yibo Shi, Jing Wang, Li Song*, Neural Rate Control for Learned Video Compression. [[paper]](https://openreview.net/forum?id=42lcaojZug)
* *Guangchi Fang, Qingyong Hu, Longguang Wang, Yulan Guo*, ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression. [[paper]](https://openreview.net/forum?id=POFrdKvpea)
* *Marlène Careil, Matthew J. Muckley, Jakob Verbeek, Stéphane Lathuilière*, Towards image compression with perfect realism at ultra-low bitrates. [[paper]](https://arxiv.org/abs/2310.10325)
* *Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato*, RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. [[paper]](https://arxiv.org/abs/2309.17182)[[code]](https://github.com/cambridge-mlg/RECOMBINER/)
* *Ivan Butakov, Alexander Tolmachev, Sofia Malanchuk, Anna Neopryatnaya, Alexey Frolov, Kirill Andreev*, Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression. [[paper]](https://arxiv.org/abs/2305.08013)
* *Han Li, Shaohui Li, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong*, FTIC: Frequency-Aware Transformer for Learned Image Compression. [[paper]](https://openreview.net/forum?id=HKGQDDTuvZ)
* *Edouard Yvinec, Arnaud Dapogny, Kevin Bailly*, Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings. [[paper]](https://arxiv.org/abs/2309.17361)
* *Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend*, Entropy Coding of Unordered Data Structures. [[paper]](https://openreview.net/forum?id=afQuNt3Ruh)[[code]](https://github.com/juliuskunze/shuffle-coding)

### ICML
* *Yiyang Ma, Wenhan Yang, Jiaying Liu*, Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder. [[paper]](https://arxiv.org/abs/2404.04916)[[code]](https://realpasu.github.io/CorrDiff_Website/)
* *Hagyeong Lee, Minkyu Kim, Jun-Hyuk Kim, Seungeon Kim, Dokwan Oh, Jaeho Lee*, Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity. [[paper]](https://arxiv.org/abs/2403.02944)[[code]](https://github.com/effl-lab/TACO)
* *Tuan Pham, Stephan Mandt*, Neural NeRF Compression. [[paper]](https://arxiv.org/abs/2406.08943)
* *Hyomin Kim, Yunhui Jang, Jaeho Lee, Sungsoo Ahn*, Hybrid Neural Representations for Spherical Data. [[paper]](https://arxiv.org/abs/2402.05965)
* *Chen Zhang, Steven Tin Sui Luo, Jason Chun Lok Li, Yik-Chung Wu, Ngai Wong*, Nonparametric Teaching of Implicit Neural Representations. [[paper]](https://arxiv.org/abs/2405.10531)
* *Jiaming Zhao, Wenbo Qiao, Peng Zhang, Hui Gao*, Quantum Implicit Neural Representations. [[paper]](https://arxiv.org/abs/2406.03873)
* *Joseph Shenouda, Yamin Zhou, Robert D. Nowak*, ReLUs Are Sufficient for Learning Implicit Neural Representations. [[paper]](https://arxiv.org/abs/2406.02529)
* *Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko, Alexander Long, Simon Lucey*, A sampling theory perspective on activations for implicit neural representations. [[paper]](https://arxiv.org/abs/2402.05427)

### AAAI
* *Ming Lu, Zhihao Duan, Fengqing Zhu, Zhan Ma*, Deep Hierarchical Video Compression. [[paper]](https://arxiv.org/abs/2312.07126)
* *Huiming Zheng, Wei Gao*, End-to-End RGB-D Image Compression via Exploiting Channel-Modality Redundancy. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/28588)
* *Ao Luo, Linxin Song, Keisuke Nonaka, Kyohei Unno, Heming Sun, Masayuki Goto, Jiro Kattok*, SCP: Spherical-Coordinate-Based Learned Point Cloud Compression. [[paper]](https://arxiv.org/abs/2308.12535)[[code]](https://github.com/luoao-kddi/SCP)
* *Shilv Cai, Liqun Chen, Sheng Zhong, Luxin Yan, Jiahuan Zhou, Xu Zou*, Make Lossy Compression Meaningful for Low-Light Image. [[paper]](https://arxiv.org/html/2305.15030v3)
* *Qiuyu Duan, Zhongyun Hua, Qing Liao, Yushu Zhang, LEO Yu Zhang*, Conditional Backdoor Attack via JPEG Compression. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/29957)
* *Guangchi Fang, Qingyong Hu, Longguang Wang, Yulan Guo*, ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression. [[paper]](https://openreview.net/forum?id=POFrdKvpea)
* *Chuanbo Tang, Xihua Sheng, Zhuoyuan Li, Haotian Zhang, Li Li, Dong Liu*, Offline and Online Optical Flow Enhancement for Deep Video Compression. [[paper]](https://arxiv.org/abs/2307.05092)
* *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]](https://ojs.aaai.org/index.php/AAAI/article/view/28362)

### IJCAI
* *Sheng Shen, Huanjing Yue, Jingyu Yang*, Efficient Screen Content Image Compression via Superpixel-based Content Aggregation and Dynamic Feature Fusion.

## 2023
### CVPR
* *Jinming Liu, Heming Sun, Jiro Katto*, Learned Image Compression with Mixed Transformer-CNN Architectures. [[paper]](https://arxiv.org/abs/2303.14978)[[code]](https://github.com/jmliu206/LIC_TCM)
* *Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim*, Context-Based Trit-Plane Coding for Progressive Image Compression. [[paper]](https://arxiv.org/abs/2303.05715) [[code]](https://github.com/seungminjeon-github/CTC)
* *Eirikur Agustsson, David Minnen, George Toderici, Fabian Mentzer*, Multi-Realism Image Compression with a Conditional Generator. [[paper]](https://arxiv.org/abs/2212.13824)
* *Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng, Jinli Suo, Qionghai Dai*, TINC: Tree-structured Implicit Neural Compression. [[paper]](https://arxiv.org/abs/2211.06689)[[code]](https://github.com/RichealYoung/TINC)
* *Xi Zhang, Xiaolin Wu*, LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression. [[paper]](https://arxiv.org/abs/2304.12319)
* *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]](https://arxiv.org/abs/2302.14677)
* *Juncheol Ye, Hyunho Yeo, Jinwoo Park, Dongsu Han*, AccelIR: Task-aware Image Compression for Accelerating Neural Restoration. [[paper]](https://openaccess.thecvf.com/content/CVPR2023/html/Ye_AccelIR_Task-Aware_Image_Compression_for_Accelerating_Neural_Restoration_CVPR_2023_paper.html)[[code]](https://github.com/AccelIR-public/AccelIR-public)
* *Jiahao Li, Bin Li, Yan Lu*, Neural Video Compression with Diverse Contexts. [[paper]](https://arxiv.org/abs/2302.14402)[[code]](https://github.com/microsoft/DCVC)
* *Linfeng Qi, Jiahao Li, Bin Li, Houqiang Li, Yan Lu*, Motion Information Propagation for Neural Video Compression. [[paper]](https://openaccess.thecvf.com/content/CVPR2023/html/Qi_Motion_Information_Propagation_for_Neural_Video_Compression_CVPR_2023_paper.html)
* *Zhihao Hu, Dong Xu*, Complexity-guided Slimmable Decoder for Efficient Deep Video Compression. [[paper]](https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.html)
 * *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]](https://arxiv.org/abs/2304.02273)[[code]](https://github.com/BowenL0218/MMVC_video_codec)
* *David Alexandre, Hsueh-Ming Hang, Wen-Hsiao Peng*, Hierarchical B-frame Video Compression Using Two-Layer CANF without Motion Coding. [[paper]](https://arxiv.org/abs/2304.02690)[[code]](https://nycu-clab.github.io/)
* *Carlos Gomes, Roberto Azevedo, Christopher Schroers*, Video Compression with Entropy-Constrained Neural Representations.[[paper]](https://openaccess.thecvf.com/content/CVPR2023/html/Gomes_Video_Compression_With_Entropy-Constrained_Neural_Representations_CVPR_2023_paper.html)
* *Hao Chen, Matt Gwilliam, Ser-Nam Lim, Abhinav Shrivastava*, HNeRV: A Hybrid Neural Representation for Videos. [[paper]](https://arxiv.org/abs/2304.02633)[[code]](https://github.com/haochen-rye/HNeRV)
* *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]](https://arxiv.org/abs/2303.14124)[[code]](https://boheumd.github.io/D-NeRV/)
* *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]](https://arxiv.org/abs/2212.14593)
* *Qi Zhao, M. Salman Asif, Zhan Ma*, DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos. [[paper]](https://arxiv.org/abs/2304.06544)
* *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]](https://arxiv.org/abs/2304.04452)
* *Rui Song, Chunyang Fu, Shan Liu, Ge Li*, Efficient Hierarchical Entropy Model for Learned Point Cloud Compression. [[paper]](https://openaccess.thecvf.com/content/CVPR2023/html/Song_Efficient_Hierarchical_Entropy_Model_for_Learned_Point_Cloud_Compression_CVPR_2023_paper.html)

### ICCV
* *Yibo Yang, Stephan Mandt*, Computationally-Efficient Neural Image Compression with Shallow Decoders. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Yang_Computationally-Efficient_Neural_Image_Compression_with_Shallow_Decoders_ICCV_2023_paper.html)[[code]](https://github.com/mandt-lab/shallow-ntc)
* *Yuan Tian, Guo Lu, Guangtao Zhai, Zhiyong Gao*, Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic Compression. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Tian_Non-Semantics_Suppressed_Mask_Learning_for_Unsupervised_Video_Semantic_Compression_ICCV_2023_paper.html)
* *Jongmin Park, Jooyoung Lee, Munchurl Kim*, COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Park_COMPASS_High-Efficiency_Deep_Image_Compression_with_Arbitrary-scale_Spatial_Scalability_ICCV_2023_paper.html) [[code]](https://github.com/ImJongminPark/COMPASS)
* *Sheng Shen, Huanjing Yue, Jingyu Yang*, Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Shen_Dec-Adapter_Exploring_Efficient_Decoder-Side_Adapter_for_Bridging_Screen_Content_and_ICCV_2023_paper.html)
* *Lv Tang, Xinfeng Zhang, Gai Zhang, Xiaoqi Ma*, Scene Matters: Model-based Deep Video Compression. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Tang_Scene_Matters_Model-based_Deep_Video_Compression_ICCV_2023_paper.html)
* *Mengyao Li, Liquan Shen, Peng Ye, Guorui Feng, Zheyin Wang*, RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionary. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Li_RFD-ECNet_Extreme_Underwater_Image_Compression_with_Reference_to_Feature_Dictionary_ICCV_2023_paper.html) [[code]](https://github.com/lilala0/RFD-ECNet)
* *Sharath Girish, Abhinav Shrivastava, Kamal Gupta*, SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Girish_SHACIRA_Scalable_HAsh-grid_Compression_for_Implicit_Neural_Representations_ICCV_2023_paper.html) [[code]](https://github.com/Sharath-girish/Shacira)
* *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]](https://arxiv.org/abs/2306.05085) [[code]](https://github.com/NYCU-MAPL/TransTIC/tree/master)
* *Lvfang Tao, Wei Gao, Ge Li, Chenhao Zhang*, AdaNIC: Towards Practical Neural Image Compression via Dynamic Transform Routing. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/html/Tao_AdaNIC_Towards_Practical_Neural_Image_Compression_via_Dynamic_Transform_Routing_ICCV_2023_paper.html)
* *Ruoyu Feng, Yixin Gao, Xin Jin, Runsen Feng, Zhibo Chen*, Semantically Structured Image Compression via Irregular Group-Based Decoupling. [[paper]](https://arxiv.org/abs/2305.02586)
    
### NeurIPS
* *Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato*, Compression with Bayesian Implicit Neural Representations. [[paper]](https://arxiv.org/abs/2305.19185)[[code]](https://github.com/cambridge-mlg/combiner)
* *Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, Kundan Kumar*, High-Fidelity Audio Compression with Improved RVQGAN. [[paper]](https://arxiv.org/abs/2306.06546)[[code]](https://github.com/descriptinc/descript-audio-codec)
* *Yanghao Li, Tongda Xu, Yan Wang, Jingjing Liu, Ya-Qin Zhang*, Idempotent Learned Image Compression with Right-Inverse. [[paper]](https://openreview.net/forum?id=35dOU92OJM)
* *Gergely Flamich, Stratis Markou, Jose Miguel Hernandez Lobato*, Faster Relative Entropy Coding with Greedy Rejection Coding. [[paper]](https://arxiv.org/abs/2309.15746)
* *Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, David Bull*, HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation. [[paper]](https://arxiv.org/abs/2306.09818)[[code]](https://github.com/hmkx/HiNeRV)
* *Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura*, Neural Image Compression: Generalization, Robustness, and Spectral Biases. [[paper]](https://arxiv.org/abs/2307.08657)[[code]](https://github.com/klieberman/ood_nic)
* *Ruihan Yang, Stephan Mandt*, Lossy Image Compression with Conditional Diffusion Models. [[paper]](https://arxiv.org/abs/2209.06950)
* *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]](https://openreview.net/forum?id=1ihGy9vAIg)
* *Sadaf Salehkalaibar, Buu Phan, Jun Chen, Wei Yu, Ashish Khisti*, On the choice of Perception Loss Function for Learned Video Compression. [[paper]](https://arxiv.org/abs/2305.19301)
* *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]](https://arxiv.org/abs/2305.15523)[[code]](https://github.com/UTAustin-SwarmLab/Task-aware-Distributed-Source-Coding)
* *Haoyu Guo, Sida Peng, Yunzhi Yan, Linzhan Mou, Yujun Shen, Hujun Bao, Xiaowei Zhou*, Compact Neural Volumetric Video Representations with Dynamic Codebooks. [[paper]](https://openreview.net/forum?id=xTgM7XLN9P)[[code]](https://github.com/zju3dv/compact_vv)

### ICLR
* *Xinjie Zhang, Jiawei Shao, Jun Zhang*, LDMIC: Learning-based Distributed Multi-view Image Coding. [[paper]](https://openreview.net/forum?id=ILQVw4cA5F9)[[code]](https://github.com/Xinjie-Q/LDMIC)
* *Jinxi Xiang, Kuan Tian, Jun Zhang*, MIMT: Masked Image Modeling Transformer for Video Compression. [[paper]](https://openreview.net/forum?id=j9m-mVnndbm)
* *Langwen Huang, Torsten Hoefler*, Compressing multidimensional weather and climate data into neural networks. [[paper]](https://openreview.net/forum?id=Y5SEe3dfniJ)
* *Wang Guo-Hua, Jiahao Li, Bin Li, Yan Lu*, EVC: Towards Real-Time Neural Image Compression with Mask Decay. [[paper]](https://openreview.net/forum?id=XUxad2Gj40n)[[code]](https://github.com/microsoft/DCVC)

### 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]](https://arxiv.org/abs/2209.09422)[[code]](https://github.com/tongdaxu/Bit-Allocation-Using-Optimization)
* *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]](https://arxiv.org/abs/2301.11189)
* *Hee Min Choi, Hyoa Kang, Dokwan Oh*, Is Overfitting Necessary for Implicit Video Representation?[[paper]](https://openreview.net/forum?id=JuNIuHLm9y)

### 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]](https://arxiv.org/pdf/2209.02514.pdf)
* *Mingyue Cui, Junhua Long, Mingjian Feng, Boyang Li, Huang Kai*, OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression with Local Enhancement. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/25121)[[code]](https://github.com/octree-nn/octformer)
* *Xuhao Jiang, Weimin Tan, Tian Tan, Bo Yan, Liquan Shen*, Multi-Modality Deep Network for Extreme Learned Image Compression. [[paper]](https://arxiv.org/abs/2304.13583)
* *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]](https://ojs.aaai.org/index.php/AAAI/article/view/25458) [[code]](https://github.com/WilliammmZ/STCF)
* *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]](https://ojs.aaai.org/index.php/AAAI/article/view/25469)[[code]](https://github.com/beimingli0626/tinyNeRF)
* *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]](https://ojs.aaai.org/index.php/AAAI/article/view/25543)
* *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]](https://arxiv.org/abs/2209.15180v5) [[code]](https://github.com/RichealYoung/ImplicitNeuralCompression)
* *Jinhai Yang, Mengxi Guo, Shijie Zhao, Junlin Li, Li Zhang*, Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling. [[paper]](https://arxiv.org/abs/2303.02353) [[code]](https://github.com/yang-jin-hai/SAIN)

## 2022
### CVPR
* *Tom Ryder, Chen Zhang, Ning Kang, Shifeng Zhang*, Split Hierarchical Variational Compression. [[paper]](https://arxiv.org/abs/2204.02071)
* *Zhihao Hu, Guo Lu, Jinyang Guo, Shan Liu, Wei Jiang, Dong Xu*, Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction. [[paper]](https://arxiv.org/abs/2206.07460)
* *Jun-Hyuk Kim, Byeongho Heo, Jong-Seok Lee*, Joint Global and Local Hierarchical Priors for Learned Image Compression. [[paper]](https://arxiv.org/abs/2112.04487)[[code]](https://github.com/naver-ai/informer)
* *Hochang Rhee, Yeong Il Jang, Seyun Kim, Nam Ik Cho*, LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network. [[paper]](https://arxiv.org/abs/2112.06417)
* *Jae-Han Lee, Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim*, DPICT: Deep Progressive Image Compression Using Trit-Planes. [[paper]](https://arxiv.org/abs/2112.06334)[[code]](https://github.com/jaehanlee-mcl/DPICT)
* *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]](https://arxiv.org/abs/2203.16357)
* *Xiaosu Zhu, Jingkuan Song, Lianli Gao, Feng Zheng, Heng Tao Shen*, Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression. [[paper]](https://arxiv.org/abs/2203.10897)[[code]](https://github.com/xiaosu-zhu/McQuic)
* *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]](https://arxiv.org/abs/2203.10886)
* *Renjie Zou, Chunfeng Song, Zhaoxiang Zhang*, The Devil Is in the Details: Window-based Attention for Image Compression. [[paper]](https://arxiv.org/abs/2203.08450)[[code]](https://github.com/Googolxx/STF)
* *Dezhao Wang, Wenhan Yang, Yueyu Hu, Jiaying Liu*, Neural Data-Dependent Transform for Learned Image Compression. [[paper]](https://arxiv.org/abs/2203.04963)[[code]](https://dezhao-wang.github.io/Neural-Syntax-Website/)
* *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]](https://openaccess.thecvf.com/content/CVPR2022/html/Kang_PILC_Practical_Image_Lossless_Compression_With_an_End-to-End_GPU_Oriented_CVPR_2022_paper.html)
* *Zhenghao Chen, Guo Lu, Zhihao Hu, Shan Liu, Wei Jiang, Dong Xu*, LSVC: A Learning-based Stereo Video Compression Framework. [[paper]](https://openaccess.thecvf.com/content/CVPR2022/html/Chen_LSVC_A_Learning-Based_Stereo_Video_Compression_Framework_CVPR_2022_paper.html)
* *Jianjun Lei, Xiangrui Liu, Bo Peng, Dengchao Jin, Wanqing Li, Jingxiao Gu*, Deep Stereo Image Compression via Bi-directional Coding. [[paper]](https://openaccess.thecvf.com/content/CVPR2022/html/Lei_Deep_Stereo_Image_Compression_via_Bi-Directional_Coding_CVPR_2022_paper.html)
* *Matthias Wödlinger, Jan Kotera, Jan Xu, Robert Sablatnig*, SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention. [[paper]](https://openaccess.thecvf.com/content/CVPR2022/html/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.html)[[code]](https://github.com/mwoedlinger/sasic)
* *Xuanyu Zhou, Charles R. Qi, Yin Zhou, Dragomir Anguelov*, RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding. [[paper]](https://paperswithcode.com/paper/lidar-range-image-compression-with-deep-delta)
* *Yun He, Xinlin Ren, Danhang Tang, Yinda Zhang, Xiangyang Xue, Yanwei Fu*, Density-preserving Deep Point Cloud Compression. [[paper]](https://arxiv.org/abs/2204.12684) [[project]](https://yunhe20.github.io/D-PCC/)
* *Guangchi Fang, Qingyong Hu, Hanyun Wang, Yiling Xu, Yulan Guo*, 3DAC: Learning Attribute Compression for Point Clouds. [[paper]](https://arxiv.org/abs/2203.09931)[[code]](https://github.com/fatPeter/ThreeDAC)
* *Guo Lu, Tianxiong Zhong, Jing Geng, Qiang Hu, Dong Xu*, Learning based Multi-modality Image and Video Compression. [[paper]](https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Learning_Based_Multi-Modality_Image_and_Video_Compression_CVPR_2022_paper.html)

### ECCV
* *Yannick Strümpler, Janis Postels, Ren Yang, Luc van Gool, Federico Tombari*, Implicit Neural Representations for Image Compression. [[paper]](https://arxiv.org/abs/2112.04267)
* *Joaquim Campos, Simon Meierhans, Abdelaziz Djelouah, Christopher Schroers*, Content Adaptive Latents and Decoder for Neural Image Compression. [[paper]](https://arxiv.org/abs/1906.01223)
* *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]](https://arxiv.org/abs/2203.02452v2)
* *Chajin Shin, Hyeongmin Lee, Hanbin Son, Sangjin Lee, Dogyoon Lee, Sangyoun Lee*, Expanded Adaptive Scaling Normalization for End to End Image Compression. [[paper]](https://arxiv.org/abs/2208.03049)[[code]](https://github.com/ChajinShin/EASN)
* *Tianyi Liu, Sen He, Vinodh Kumaran Jayakumar, Wei Wang*, A Cloud 3D Dataset and Application-Specific Learned Image Compression in Cloud 3D. [[paper]](https://link.springer.com/chapter/10.1007/978-3-031-19839-7_16) [[code]](https://github.com/cloud-graphics-rendering/AppSpecificLIC)
* *Meng Li, Shangyin Gao, Yihui Feng, Yibo Shi, Jing Wang*, Content-Oriented Learned Image Compression. [[paper]](https://arxiv.org/abs/2207.14168v2) [[code]](https://github.com/lmijydyb/COLIC)
* *Yibo Shi, Yunying Ge, Jing Wang, Jue Mao*, AlphaVC: High-Performance and Efficient Learned Video Compression. [[paper]](https://arxiv.org/abs/2207.14678) 
* *Yung-Han Ho, Chih-Peng Chang, Peng-Yu Chen, Alessandro Gnutti, Wen-Hsiao Peng*, CANF-VC: Conditional Augmented Normalizing Flows for Video Compression. [[paper]](https://arxiv.org/abs/2207.05315)[[code]](https://github.com/NYCU-MAPL/CANF-VC)
* *Fabian Mentzer, Eirikur Agustsson, Johannes Ballé, David Minnen, Nick Johnston, George Toderici*, Neural Video Compression using GANs for Detail Synthesis and Propagation. [[paper]](https://arxiv.org/abs/2107.12038v3) 
* *Zhili Chen, Zian Qian, Sukai Wang, Qifeng Chen*, Point Cloud Compression with Sibling Context and Surface Priors. [[paper]](https://link.springer.com/chapter/10.1007/978-3-031-19839-7_43) [[code]](https://github.com/zlichen/PCC-S)
* *Sukai Wang, Ming Liu*, Point Cloud Compression using Range Image-based Entropy Model for Autonomous Driving. [[paper]](https://link.springer.com/chapter/10.1007/978-3-031-20047-2_19) 
* *Ruoyu Feng et al*, Image Coding for Machines with Omnipotent Feature Learning. [[paper]](https://arxiv.org/abs/2207.01932)
* *Ka Leong Cheng, Yueqi Xie, Qifeng Chen*, Optimizing Image Compression via Joint Learning with Denoising. [[paper]](https://arxiv.org/abs/2207.10869) [[code]](https://github.com/felixcheng97/DenoiseCompression)
* *Zizhang Li, Mengmeng Wang, Huaijin Pi, Kechun Xu, Jianbiao Mei, Yong Liu*, E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context. [[paper]](https://arxiv.org/abs/2207.08132) [[code]](https://github.com/kyleleey/E-NeRV/tree/main)

### 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]](https://openreview.net/forum?id=My5AI9aM49R)
* *Fabian Mentzer, George Toderici, David Minnen, Sung-Jin Hwang, Sergi Caelles, Mario Lucic, Eirikur Agustsson*, VCT: A Video Compression Transformer. [[paper]](https://arxiv.org/abs/2206.07307)[[code]](https://github.com/google-research/google-research/tree/master/vct)
* *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]](https://github.com/JooyoungLeeETRI/SCR)
* *Chenjian Gao, Tongda Xu, Dailan He, Hongwei Qin, Yan Wang*, Flexible Neural Image Compression via Code Editing. [[paper]](https://arxiv.org/abs/2209.09244)

### ICLR
* *Anji Liu, Stephan Mandt, Guy Van den Broeck*, Lossless Compression with Probabilistic Circuits. [[paper]](https://arxiv.org/abs/2111.11632)
* *Huan Liu, George Zhang, Jun Chen, Ashish J Khisti*, Lossy Compression with Distribution Shift as Entropy Constrained Optimal Transport. [[paper]](https://openreview.net/forum?id=BRFWxcZfAdC)
* *Yichen Qian, Ming Lin, Xiuyu Sun, Zhiyu Tan, Rong Jin*, Entroformer: A Transformer-based Entropy Model for Learned Image Compression. [[paper]](https://arxiv.org/abs/2202.05492)[[code]](https://github.com/damo-cv/entroformer)
* *Yibo Yang, Stephan Mandt*, Towards Empirical Sandwich Bounds on the Rate-Distortion Function. [[paper]](https://arxiv.org/abs/2111.12166)
* *Yinhao Zhu, Yang Yang, Taco Cohen*, Transformer-based Transform Coding. [[paper]](https://openreview.net/forum?id=IDwN6xjHnK8)
* *Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans*, Autoregressive Diffusion Models. [[paper]](https://arxiv.org/abs/2110.02037)

### 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]](https://arxiv.org/abs/2201.12857)
* *Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang*, Fast Lossless Neural Compression with Integer-Only Discrete Flows. [[paper]](https://arxiv.org/abs/2206.08869)

### 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]](https://arxiv.org/abs/2112.09300)
* *Fangdong Chen, Yumeng Xu, Li Wang*, Two-Stage Octave Residual Network for End-to-End Image Compression. [[paper]](https://www.aaai.org/AAAI22Papers/AAAI-4043.ChenF.pdf)
* *Linfeng Cao, Aofan Jiang, Wei Li, Huaying Wu, Nanyang Ye*, OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression. [[paper]](https://www.aaai.org/AAAI22Papers/AAAI-8610.CaoL.pdf) [[code]](https://github.com/caolinfeng/OoDHDR-codec)
* *Chunyang Fu, Ge Li, Rui Song, Wei Gao, Shan Liu*, OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression. [[paper]](https://arxiv.org/abs/2202.06028v1)[[code]]([https://github.com/zb12138/OctAttention])
 
### IJCAI
* *Ren Yang, Radu Timofte, Luc Van Gool*, Perceptual Learned Video Compression with Recurrent Conditional GAN. [[paper]](https://arxiv.org/abs/2109.03082)
* *Tingyu Fan, Linyao Gao, Yiling Xu, Zhu Li, Dong Wang*, D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction. [[paper]](https://arxiv.org/abs/2205.01135)[[code]](https://github.com/ttlzfhy/D-DPCC)

## 2021
### CVPR
* *Zhihao Hu, Guo Lu, Dong Xu*, FVC: A New Framework towards Deep Video Compression in Feature Space. [[paper]](https://arxiv.org/abs/2105.09600)
* *Bowen Liu, Yu Chen, Shiyu Liu, Hun-Seok Kim*, Deep Learning in Latent Space for Video Prediction and Compression. [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Deep_Learning_in_Latent_Space_for_Video_Prediction_and_Compression_CVPR_2021_paper.pdf)[[code]](https://github.com/BowenL0218/Video_Compression)
* *Aaron Chadha, Yiannis Andreopoulos*, Deep Perceptual Preprocessing for Video Coding. [[paper]](https://openaccess.thecvf.com/content/CVPR2021/html/Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper.html)
* *Shifeng Zhang, Chen Zhang, Ning Kang, Zhenguo Li*, iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression. [[paper]](https://arxiv.org/abs/2103.16211)
* *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]](https://arxiv.org/abs/2012.01874)
* *Xi Zhang, Xiaolin Wu*, Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton. [[paper]](https://arxiv.org/abs/2103.15368)
* *Dailan He, Yaoyan Zheng, Baocheng Sun, Yan Wang, Hongwei Qin*, Checkerboard Context Model for Efficient Learned Image Compression. [[paper]](https://arxiv.org/abs/2103.15306)[[code]](https://github.com/JiangWeibeta/Checkerboard-Context-Model-for-Efficient-Learned-Image-Compression)
* *Fei Yang, Luis Herranz, Yongmei Cheng, Mikhail G. Mozerov*, Slimmable Compressive Autoencoders for Practical Neural Image Compression. [[paper]](https://arxiv.org/abs/2103.15726)
* *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]](https://arxiv.org/abs/2103.17015)
* *Ze Cui, Jing Wang, Shangyin Gao, Tiansheng Guo, Yihui Feng, Bo Bai*, Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation. [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Cui_Asymmetric_Gained_Deep_Image_Compression_With_Continuous_Rate_Adaptation_CVPR_2021_paper.pdf)[[code]](https://github.com/mmSir/GainedVAE)
* *Yuval Bahat, Tomer Michaeli*, What's in the Image? Explorable Decoding of Compressed Images. [[paper]](https://arxiv.org/abs/2006.09332)
* *Xin Deng, Wenzhe Yang, Ren Yang, Mai Xu, Enpeng Liu, Qianhan Feng, Radu Timofte*, Deep Homography for Efficient Stereo Image Compression. [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper.pdf)[[code]](https://github.com/ywz978020607/HESIC)
* *Zizheng Que, Guo Lu, Dong Xu*, VoxelContext-Net: An Octree Based Framework for Point Cloud Compression. [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Que_VoxelContext-Net_An_Octree_Based_Framework_for_Point_Cloud_Compression_CVPR_2021_paper.pdf)
* *Chi D. K. Pham, Chen Fu, Jinjia Zhou*, Deep Learning Based Spatial-Temporal In-Loop Filtering for Versatile Video Coding. [[workshop]](https://openaccess.thecvf.com/content/CVPR2021W/CLIC/papers/Pham_Deep_Learning_Based_Spatial-Temporal_In-Loop_Filtering_for_Versatile_Video_Coding_CVPRW_2021_paper.pdf)

### ICCV
* *Jan P. Klopp, Keng-Chi Liu, Shao-Yi Chien, Liang-Gee Chen*, Online-Trained Upsampler for Deep Low Complexity Video Compression. [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Klopp_Online-Trained_Upsampler_for_Deep_Low_Complexity_Video_Compression_ICCV_2021_paper.pdf)
* *Reza Pourreza, Taco S Cohen*, Extending Neural P-Frame Codecs for B-Frame Coding. [[paper]](https://arxiv.org/abs/2104.00531)
* *Mehrdad Khani, Vibhaalakshmi Sivaraman, Mohammad Alizadeh*, Efficient Video Compression via Content-Adaptive Super-Resolution. [[paper]](https://arxiv.org/abs/2104.02322)[[code]](https://github.com/AdaptiveVC/SRVC)
* *Oren Rippel, Alexander G. Anderson, Kedar Tatwawadi, Sanjay Nair, Craig Lytle, Lubomir Bourdev*, ELF-VC: Efficient Learned Flexible-Rate Video Coding. [[paper]](https://arxiv.org/abs/2104.14335v1)[[project]](https://www.wave.one/elf)
* *Xueyang Fu, Xi Wang, Aiping Liu, Junwei Han, Zheng-Jun Zha*, Learning Dual Priors for JPEG Compression Artifacts Removal. [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Fu_Learning_Dual_Priors_for_JPEG_Compression_Artifacts_Removal_ICCV_2021_paper.pdf)
* *Myungseo Song, Jinyoung Choi, Bohyung Han*, Variable-Rate Deep Image Compression Through Spatially-Adaptive Feature Transform. [[paper]](https://arxiv.org/abs/2108.09551)[[code]](https://github.com/micmic123/QmapCompression)
* *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]](https://openaccess.thecvf.com/content/ICCV2021/papers/Gao_Neural_Image_Compression_via_Attentional_Multi-Scale_Back_Projection_and_Frequency_ICCV_2021_paper.pdf)

### NeurIPS
* *Jiahao Li, Bin Li, Yan Lu*, Deep Contextual Video Compression. [[paper]](https://arxiv.org/abs/2109.15047v2)
* *Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li*, iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder. [[paper]](https://arxiv.org/abs/2111.00965)
* *George Zhang, Jingjing Qian, Jun Chen, Ashish Khisti*, Universal Rate-Distortion-Perception Representations for Lossy Compression. [[paper]](https://arxiv.org/abs/2106.10311)
* *Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison*, Lossy Compression for Lossless Prediction. [[paper]](https://arxiv.org/abs/2106.10800)
* *Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li*, OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression. [[paper]](https://arxiv.org/abs/2111.01662v1)
* *Siddharth Reddy, Anca D. Dragan, Sergey Levine*, Pragmatic Image Compression for Human-in-the-Loop Decision-Making. [[paper]](https://arxiv.org/abs/2108.04219)
* *Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava*, NeRV: Neural Representations for Videos. [[paper]](https://arxiv.org/abs/2110.13903)[[code]](https://github.com/haochen-rye/NeRV/tree/master)

### ICLR
* *Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt*, Hierarchical Autoregressive Modeling for Neural Video Compression. [[paper]](https://arxiv.org/abs/2010.10258)[[code]](https://github.com/privateyoung/Youtube-NT)
* *Ties van Rozendaal, Iris A.M. Huijben, Taco S. Cohen*, Overfitting for Fun and Profit: Instance-Adaptive Data Compression . [[paper]](https://arxiv.org/abs/2101.08687)
* *Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon*, Improved Autoregressive Modeling with Distribution Smoothing. [[paper]](https://arxiv.org/abs/2103.15089)
* *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]](https://arxiv.org/abs/2006.12459v2)
* *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]](https://arxiv.org/abs/2010.08321)[[code]](https://github.com/damo-cv/img-comp-reference)

### ICML
* *Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen*, Soft then Hard: Rethinking the Quantization in Neural Image Compression. [[paper]](https://arxiv.org/abs/2104.05168)
* *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]](https://arxiv.org/abs/2102.11086)
* *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]](https://arxiv.org/abs/2106.02782)
* *Ruihan Yang, Yibo Yang, Joseph Marino, Yang Yang, and Stephan Mandt*, Deep generative video compression with temporal autoregressive transforms. [[workshop]](https://invertibleworkshop.github.io/INNF_2020/accepted_papers/pdfs/39.pdf)

### AAAI
* *Mohammad Akbari, Jie Liang, Jingning Han, Chengjie Tu*, Learned Bi-Resolution Image Coding Using Generalized Octave Convolutions. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16816)

## 2020
### CVPR
* *Jianping Lin, Dong Liu, Houqiang Li, Feng Wu*, M-LVC: Multiple Frames Prediction for Learned Video Compression. [[paper]](https://arxiv.org/abs/2004.10290)[[code]](https://github.com/JianpingLin/M-LVC_CVPR2020)
* *Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte*, Learning for Video Compression With Hierarchical Quality and Recurrent Enhancement. [[paper]](https://arxiv.org/abs/2003.01966)[[code]](https://github.com/RenYang-home/HLVC)
* *Eirikur Agustsson, David Minnen, Nick Johnston, Johannes Balle, Sung Jin Hwang, George Toderici*, Scale-Space Flow for End-to-End Optimized Video Compression. [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Agustsson_Scale-Space_Flow_for_End-to-End_Optimized_Video_Compression_CVPR_2020_paper.pdf)
* *Rongjie Liu, Meng Li, Li Ma*, Efficient in-situ image and video compression through probabilistic image representation. [[paper]](https://arxiv.org/abs/1912.05622v3)
* *Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto*, Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules. [[paper]](https://arxiv.org/abs/2001.01568)
* *Fabian Mentzer, Luc Van Gool, Michael Tschannen*, Learning Better Lossless Compression Using Lossy Compression. [[paper]](https://arxiv.org/abs/2003.10184)
* *Chaoyi Lin, Jiabao Yao, Fangdong Chen, Li Wang*, A Spatial RNN Codec for End-to-End Image Compression. [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_A_Spatial_RNN_Codec_for_End-to-End_Image_Compression_CVPR_2020_paper.pdf)
* *Innfarn Yoo, Xiyang Luo, Yilin Wang, Feng Yang, Peyman Milanfar*, GIFnets: Differentiable GIF Encoding Framework. [[paper]](https://arxiv.org/abs/2006.13434)
* *Danhang Tang et. al*, Deep Implicit Volume Compression. [[paper]](https://arxiv.org/abs/2005.08877)
* *Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, Raquel Urtasun*, OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression. [[paper]](https://arxiv.org/abs/2005.07178v2)

### ECCV
* *Zhihao Hu, Zhenghao Chen, Dong Xu, Guo Lu, Wanli Ouyang, Shuhang Gu*, Improving Deep Video Compression by Resolution-adaptive Flow Coding. [[paper]](https://arxiv.org/abs/2009.05982v1)
* *Guo Lu, Chunlei Cai, Xiaoyun Zhang, Li Chen, Wanli Ouyang, Dong Xu, Zhiyong Gao*, Content Adaptive and Error Propagation Aware Deep Video Compression. [[paper]](https://arxiv.org/abs/2003.11282v1)
* *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]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750239.pdf)
* *Jerry Liu, Shenlong Wang, Wei-Chiu Ma, Meet Shah, Rui Hu, Pranaab Dhawan, Raquel Urtasun*, Conditional Entropy Coding for Efficient Video Compression. [[paper]](https://arxiv.org/abs/2008.09180)
* *Sharon Ayzik, Shai Avidan*, Deep Image Compression using Decoder Side Information. [[paper]](https://arxiv.org/abs/2001.04753) [[code]](https://github.com/ayziksha/DSIN)
* *Jinyoung Choi, Bohyung Han*, Task-Aware Quantization Network for JPEG Image Compression. [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-58565-5_19)

### NeurIPS
* *Yibo Yang, Robert Bamler, Stephan Mandt*, Improving Inference for Neural Image Compression. [[paper]](https://arxiv.org/abs/2006.04240)
* *Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson*, High-Fidelity Generative Image Compression. [[paper]](https://arxiv.org/abs/2006.09965)[[code]](https://github.com/Justin-Tan/high-fidelity-generative-compression)
* *Eirikur Agustsson, Lucas Theis*, Universally Quantized Neural Compression. [[paper]](https://arxiv.org/abs/2006.09952)
* *Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato*, Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. [[paper]](https://arxiv.org/abs/2010.01185)
* *Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun*, MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models. [[paper]](https://arxiv.org/abs/2011.07590)

### ICLR
* *James Townsend, Thomas Bird, Julius Kunze, David Barber*, HiLLoC: lossless image compression with hierarchical latent variable models . [[paper]](https://arxiv.org/abs/1912.09953)

### ICML
* *Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse*, Evaluating Lossy Compression Rates of Deep Generative Models. [[paper]](https://arxiv.org/abs/2008.06653)

### AAAI
* *Haojie Liu, Han shen, Lichao Huang, Ming Lu, Tong Chen, Zhan Ma*, Learned Video Compression via Joint Spatial-Temporal Correlation Exploration. [[paper]](https://arxiv.org/abs/1912.06348)
* *Yueyu Hu, Wenhan Yang, Jiaying Liu*, Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/6736)

### IJCAI
* *Zhisheng Zhong, Hiroaki Akutsu, Kiyoharu Aizawa*, Channel-Level Variable Quantization Network for Deep Image Compression. [[paper]](https://www.ijcai.org/proceedings/2020/0065.pdf)
* *Menglu Wang, Xueyang Fu, Zepei Sun, Zheng-Jun Zha*, JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks. [[paper]](https://www.ijcai.org/proceedings/2020/0079.pdf)

## 2019
### CVPR
* *Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto*, Learning Image and Video Compression Through Spatial-Temporal Energy Compaction. [[paper]](https://arxiv.org/abs/1906.09683)
* *Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, Zhiyong Gao*, DVC: An End-To-End Deep Video Compression Framework. [[paper]](https://arxiv.org/abs/1812.00101)
* *Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool*, Practical Full Resolution Learned Lossless Image Compression. [[paper]](https://arxiv.org/abs/1811.12817)
* *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]](https://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Machine_Vision_Guided_3D_Medical_Image_Compression_for_Efficient_Transmission_CVPR_2019_paper.html)

### ICCV
* *Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander G. Anderson, Lubomir Bourdev*, Learned Video Compression. [[paper]](https://openaccess.thecvf.com/content_ICCV_2019/html/Rippel_Learned_Video_Compression_ICCV_2019_paper.html)
* *Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool*, Generative Adversarial Networks for Extreme Learned Image Compression. [[paper]](https://arxiv.org/abs/1804.02958)[[code]](https://github.com/PanPey/GAN-based-Image-Compressor-System)
* *Jerry Liu, Shenlong Wang, Raquel Urtasun*, DSIC: Deep Stereo Image Compression. [[paper]](https://arxiv.org/abs/1908.03631)
* *Yoojin Choi, Mostafa El-Khamy, Jungwon Lee*, Variable Rate Deep Image Compression With a Conditional Autoencoder. [[paper]](https://arxiv.org/abs/1909.04802)
* *Xueyang Fu, Zheng-Jun Zha, Feng Wu, Xinghao Ding, John Paisley*, JPEG Artifacts Reduction via Deep Convolutional Sparse Coding. [[paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Fu_JPEG_Artifacts_Reduction_via_Deep_Convolutional_Sparse_Coding_ICCV_2019_paper.pdf)

### NeurIPS
* *Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt*, Deep Generative Video Compression. [[paper]](https://arxiv.org/abs/1810.02845)
* *Emiel Hoogeboom, Jorn W.T. Peters, Rianne van den Berg, Max Welling*, Integer Discrete Flows and Lossless Compression. [[paper]](https://arxiv.org/abs/1905.07376)
* *Jonathan Ho, Evan Lohn, Pieter Abbeel*, Compression with Flows via Local Bits-Back Coding. [[paper]](https://arxiv.org/abs/1905.08500v3)
 
### ICLR
* *Johannes Ballé, Nick Johnston, David Minnen*, Integer Networks for Data Compression with Latent-Variable Models. [[paper]](https://openreview.net/forum?id=S1zz2i0cY7)[[code]](https://github.com/EccHui/Integer-Hyper-prior-Network) [[code]](https://github.com/EccHui/Integer-Hyper-prior-Network)
* *James Townsend, Thomas Bird, David Barber*, Practical lossless compression with latent variables using bits back coding . [[paper]](https://openreview.net/forum?id=ryE98iR5tm)[[code]](https://github.com/bits-back/bits-back)
* *Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack*, Context-adaptive Entropy Model for End-to-end Optimized Image Compression. [[paper]](https://openreview.net/forum?id=HyxKIiAqYQ)[[code]](https://github.com/JooyoungLeeETRI/CA_Entropy_Model)
  
### ICML
* *Yochai Blau, Tomer Michaeli*, Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff. [[paper]](https://arxiv.org/abs/1901.07821)
* *Friso H. Kingma, Pieter Abbeel, Jonathan Ho*, Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables. [[paper]](https://arxiv.org/abs/1905.06845)[[code]](https://github.com/fhkingma/bitswap)
* *Yan Wu, Mihaela Rosca, Timothy Lillicrap*, Deep Compressed Sensing. [[paper]](https://arxiv.org/abs/1905.06723)
* *Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon*, Neural Joint Source-Channel Coding. [[paper]](https://arxiv.org/abs/1811.07557)[[code]](https://github.com/ermongroup/necst)

### 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]](https://ojs.aaai.org/index.php/AAAI/article/view/4546)

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