Diffusion-Based Image Compression

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8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Flux as a Codec: Zero-Shot Lossy Compression with Flux
Nov 5, 2026Fuma Kimishima, Jinjia ZhouDiffusion-Based Image Compression Generative Image Compression
Hosei University, Tokyo, Japan
BitFlat: Error-Localized Bitstream Design for Robust Diffusion Image Compression
Oct 19, 2026Xinfeng Deng, Li Zhou, Haitao Chen +5Diffusion-Based Image Compression Generative Image Compression
College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.
Rethinking Generative Image Compression at Extremely Low Bitrates
Sep 30, 2026Tianyu Zhang, Zhaoyang Jia, Houqiang Li +1Learned Image Compression Autoencoders
University of Science and Technology of China
Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization
Jun 11, 2026Sanxin Jiang, Jiro Katto, Heming SunLearned Image Compression Diffusion-Based Image Compression
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
Exploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression
Jun 1, 2026Hao Wei, Yanhui Zhou, Chenyang Ge +2Learned Image Compression Diffusion-Based Image Compression
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
A Noise Constrained Diffusion (NC-Diffusion) Framework for High Fidelity Image Compression
Apr 8, 2026Zhenyu Du, Yanbo Gao, Shuai Li +3Diffusion-Based Image Compression Learned Image Compression
Shandong University, Jinan, China · University of Electronic Science and Technology of China, Sichuan, China
A PPO-Based Bitrate Allocation Conditional Diffusion Model for Remote Sensing Image Compression
Mar 16, 2026Yuming Han, Jooho Kim, Anish ShakyaDiffusion-Based Image Compression Learned Image Compression
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843 USA · Institute for a Disaster Resilient Texas, Texas A&M University, College Station, TX 77843 USA · Department of Marine & Coastal Environmental Science, Texas A&M University, Galveston, TX 77554 USA
DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression
Mar 13, 2026Junqi Shi, Ming Lu, Xingchen Li +3Diffusion-Based Image Compression Learned Image Compression
School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China

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Flux as a Codec: Zero-Shot Lossy Compression with Flux

Fuma Kimishima, Jinjia Zhou

Hosei University, Tokyo, Japan

Diffusion-Based Image Compression Generative Image Compression Learned Image Compression Diffusion Models

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We propose FluxC, a zero-shot codec that uses Flux as a compression prior. Although DiffC has shown that pretrained diffusion models can serve as zero-shot codecs, we ask how the choice of coding path and reconstruction method affects rate--distortion--perception behavior. We analyze FluxC along three axes: coding cost and bit allocation, reconstruction method, and low rate distortion. At matched signal-to-noise ratio (SNR), we show that the path choice does not affect the ideal information rate, but changes how bits are allocated across time steps. For reconstruction, we compare ancestral sampling, one-step ODE reconstruction, and many-step ODE reconstruction. Our analysis shows that ODE-based reconstruction can achieve lower distortion than ancestral sampling, especially in the low rate regime. Experiments on Gaussian sources and natural images support this prediction. Moreover, FluxC performs competitively with, and in some cases outperforms, recent neural image compression methods, suggesting that Flux can serve as a principled zero-shot codec.