Unified Learning-Based Lossy and Lossless Jpeg Recompression

Author:

Zhang Jianghui1,Wang Yuanyuan2,Guo Lina2,Luo Jixiang2,Xu Tongda1,Wang Yan1,Wang Zhi1,Qin Hongwei2

Affiliation:

1. Tsinghua University

2. SenseTime Research

Funder

Baidu

Publisher

IEEE

Reference22 articles.

1. The design, implementation, and deployment of a system to transparently compress hundreds of petabytes of image files for a file-storage service;horn;14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17),2017

2. ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding

3. Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain

4. Jpeg xl next-generation image compression architecture and coding tools;alakuijala;SPIE Appl Digit Image Process XLIII,2019

5. Idf++: Analyzing and improving integer discrete flows for lossless compression;van den berg;International Conference on Learning Representations,2021

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1. Forgery detection using polynomial fitting in recompressed JPEG images;Signal, Image and Video Processing;2023-12-24

2. Novel Symbol over Multiplexing Technique for Transmitted Power, Bandwidth and Computational Load Reduction;2023 International Conference on Engineering Applied and Nano Sciences (ICEANS);2023-10-25

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