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