Learned Image Compression

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5 papers in the last four weeks, down 29% on the four weeks before. 0.0% of all new papers.

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MambaRaw: Selective State Space Modeling for Efficient 4K Raw Image Reconstruction
Jun 23, 2026Peize Li, Fanhu Zeng, Tongda Xu +5Learned Image Compression Image Reconstruction
Institute for AI Industry Research (AIR), Tsinghua University · King’s College London · Dalian University of Technology +3
PaaF: Raising the perceived quality of INR-Based Image Compression
Jun 19, 2026Lorenzo Catania, Dario AllegraLearned Image Compression Implicit Neural Representations
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts
Jun 19, 2026Jiancheng Zhao, Xiang Ji, Yifan Zhan +2Learned Image Compression Mixture-of-Experts Models
The University of Tokyo
Gen2-IC: Bridging Generative Models and Image Codecs through Latent Transport
Jun 19, 2026Yinhuan Huang, Hao Cao, Pu chen +3Learned Image Compression Generative Image Compression
Tsinghua University
Learned Image Compression for Vision-Language-Action Models
Jun 15, 2026Hyeonjun Kim, Jegwang Ryu, Sangbeom Ha +4Learned Image Compression Vision-Language-Action Models
POSTECH · Soongsil University · Chung-Ang University
Learned JPEG Compression for DNN Vision
Jun 15, 2026Kaixiang Zheng, Ahmed H. Salamah, Siyu Chen +1Learned Image Compression
Department of Electrical and Computer Engineering, University of Waterloo
Variable-Rate Deep Image Compression based on Low-Rank Adaptation by Progressive Learning
Jun 15, 2026Xing-Yu Xu, Chen-Hsiu Huang, Ja-Ling WuLearned Image Compression Low-Rank Adaptation
Communications and Multimedia Lab National Taiwan University Taipei, Taiwan
Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization
Jun 11, 2026Sanxin Jiang, Jiro Katto, Heming SunLearned Image Compression Rate-Distortion Optimization
Department of Information Engineering Shanghai University of Electric Power Shanghai, China · Department of Computer Science and Communication Engineering Waseda University Tokyo, Japan · Faculty of Engineering Institute of Science Tokyo Tokyo, Japan
Few-step Generative Models as Lossy Compression
Jun 9, 2026Fuma Kimishima, Jinjia ZhouLearned Image Compression Flow-Based Generative Modeling
Hosei University, Tokyo, Japan
Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission
Jun 4, 2026Tianqi Ren, Rongpeng Li, Xianfu Chen +2Learned Image Compression Diffusion Language Models
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China · Shenzhen CyberAray Network Technology Co., Ltd, Shenzhen 518000, China · School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China +1
Balancing Image Compression and Generation with Bootstrapped Tokenization
Jun 4, 2026Haozhe Chi, Jinghan Li, Hao Jiang +4Learned Image Compression Image Tokenization
1Peking University · 2Central Media Technology Institute, Huawei
SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction
Jun 2, 2026Dan Jacobellis, Neeraja J. YadwadkarLearned Image Compression Autoencoders
Department of Electrical and Computer Engineering The University of Texas at Austin Austin, TX 78712, USA
Exploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression
Jun 1, 2026Hao Wei, Yanhui Zhou, Chenyang Ge +2Learned Image Compression Diffusion Models
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China · School of Information and Telecommunication, Xi’an Jiaotong University, Xi’an 710049, China · Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Crawley, WA 6009, Australia
Training-Free Continuous Bitrate Control for Scalable Image Coding for Humans and Machines
May 29, 2026Yui Tatsumi, Hiroshi WatanabeLearned Image Compression
Graduate School of FSE, Waseda University Tokyo, Japan
ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
May 27, 2026Haisheng Fu, Runyu Yang, Feng Ding +5Learned Image Compression Hybrid CNN-Transformer Architectures
Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC, Canada · School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada · School of Electronic Science and Technology, Eastern Institute of Technology, Ningbo, China +1
Efficient Learned Image Compression without Entropy Coding
May 22, 2026Hao Cao, Wenqi Guo, Zhijin Qin +1Learned Image Compression Vector Quantization
Department of Electronic Engineering, Tsinghua University · Department of Automation, Tsinghua University · State Key Laboratory of Space Network and Communications +1
Distributed Image Compression with Multimodal Side Information at Extremely Low Bitrates
May 21, 2026Guojun Xu, Mingyang Zhang, Jianwen Xiang +3Learned Image Compression
School of Computer Science and Artificial Intelligence, Wuhan University of Technology
Deep learning-based compression of giga-resolution whole slide images
May 17, 2026Maren Høibø, Etienne Gaucher, Ingerid Reinertsen +2Learned Image Compression Histopathology Image Segmentation
Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway · Department of Health Research, SINTEF Digital, NO-7465 Trondheim, Norway · Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway +1
Adaptive Fused Prior Transfer for Controllable Generative Image Compression
May 16, 2026Yifei Pei, Ying Liu, Nam LingLearned Image Compression Generative Image Compression
Department of Computer Science and Engineering, Santa Clara University, Santa Clara, CA 95053 USA
Dual-Latent Collaborative Decoding for Fidelity-Perception Balanced Image Compression
May 14, 2026Qi Mao, Zijian Wang, Zhengxue Cheng +2Learned Image Compression Mixture of Experts
School of Information and Communication Engineering and the State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China · School of Information Science and Electronic Engineering, Shanghai Jiao Tong University · Department of Computer Science, City University of Hong Kong +1
Qwen-Image-VAE-2.0 Technical Report
May 13, 2026Zekai Zhang, Deqing Li, Kuan Cao +27Variational Autoencoders Learned Image Compression
Qwen Team
Mutual Enhancement Between Global Tokens and Patch Tokens: From Theory to Practice
May 11, 2026Xiusheng Huang, Xin Jiang, Jun Zhao +2Learned Image Compression Image Tokenization
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence
Cross-Domain Lossy Compression via Constrained Minimum Entropy Coupling
May 11, 2026Nam Nguyen, Hassan Tavakoli, An Vuong +2Learned Image Compression Rate-Distortion Optimization
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, 97331
Kelvin v1.0: A Neural Pre-Encoder for H.264: A standards-compliant learned preprocessor with -27.62% BD-VMAF on UVG
May 10, 2026Marco GrazianoLearned Image Compression Video Compression
Graziano Labs Corp., Palo Alto, CA, USA.
Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression
May 9, 2026Jiaming Liang, Chi-Man Pun, Weisi Lin +1Learned Image Compression Adversarial Attacks
University of Macau Macau, China · Nanyang Technological University Singapore
Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction
May 9, 2026Jinxiao Zhang, Runmin Dong, Xiyong Wu +14Learned Image Compression Generative Image Compression
Department of Earth System Science, Tsinghua University · Sun Yat-Sen University · Institute of Data and Information, Tsinghua Shenzhen International Graduate School +2
Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification
May 8, 2026Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok LeeLearned Image Compression
School of Integrated Technology, Yonsei University · BK21 Graduate Program in Intelligent Semiconductor Technology, Yonsei University · Department of Artificial Intelligence, Chung-Ang University
LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation
May 7, 2026Dan Jacobellis, Neeraja J. YadwadkarLearned Image Compression Rate-Distortion Optimization
University of Texas at Austin
What Matters in Practical Learned Image Compression
May 6, 2026Kedar Tatwawadi, Parisa Rahimzadeh, Zhanghao Sun +5Learned Image Compression Rate-Distortion Optimization
Apple
SAMIC: A Lightweight Semantic-Aware Mamba for Efficient Perceptual Image Compression
May 6, 2026Jiaqian Zhang, Hao Wei, Chenyang Ge +1Learned Image Compression State Space Models
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China · School of Information and Communication Engineering, Xi’an Jiaotong University, Xi’an, China

Jun 23, 2026, cs.CVJ/K move · Enter open · S save

MambaRaw: Selective State Space Modeling for Efficient 4K Raw Image Reconstruction

Peize Li, Fanhu Zeng, Tongda Xu, Xingguo Xu+4

Institute for AI Industry Research (AIR), Tsinghua University · King’s College London · Dalian University of Technology+3

Learned Image Compression Image Reconstruction State Space Models

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In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based reconstruction frameworks can exploit this side information when recovering raw images, their context models often become computationally expensive especially at high resolution, eg, 4K raw image, given that attention mechanisms scale quadratically with feature maps, hindering its practical application. To address these limitations, we propose MambaRaw, a JPEG-conditioned metadata-based raw image reconstruction framework that uses State Space Models (SSMs) to estimate entropy parameters efficiently. Our key contribution comprises a Spatial-Energy Coupled Context Modeling mechanism with two lightweight modules: (1) TileMambaBlock, which performs Mamba-style selective scanning only on information-dense tiles to improve the efficiency; and (2) Energy-Aware Refinement (EAR), an identity-initialized residual module that enhance feature representation to match the long-tail energy distribution of raw signals. Extensive experiments on three camera datasets (Sony, Olympus, Samsung) show consistent improvements over strong metadata-based baselines and set a new state of the art for JPEG-guided raw reconstruction with great efficiency. Notably, at low metadata bitrates, MambaRaw increases PSNR by 1.2--1.4 dB and reduces end-to-end coding latency by about 9%. Code is released at https://github.com/Peizeli1/MambaRaw.

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Latest papers 74