HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform

In recent years, there has been rapid development in learned image compression techniques that prioritize rate-distortion-perceptual compression, preserving fine details even at lower bit-rates. However, current learning-based image compression methods often sacrifice human-friendly compression and...

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Bibliographic Details
Published in2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI) pp. 188 - 194
Main Authors Ning, Peirong, Jiang, Wei, Wang, Ronggang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2023
Subjects
Artificial intelligence
Decoding
Focusing
Image coding
Image Compression
Perceptual Compression
Telecommunication computing
Transforms
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Summary:In recent years, there has been rapid development in learned image compression techniques that prioritize rate-distortion-perceptual compression, preserving fine details even at lower bit-rates. However, current learning-based image compression methods often sacrifice human-friendly compression and require long decoding times. In this paper, we propose enhancements to the backbone network and loss function of existing image compression model, focusing on improving human perception and efficiency. Our proposed approach achieves competitive subjective results compared to state-of-the-art end-to-end learned image compression methods and classic methods, while requiring less decoding time and offering human-friendly compression. Through empirical evaluation, we demonstrate the effectiveness of our proposed method in achieving outstanding performance, with more than 25% bit-rate saving with comparable perceptual quality.
DOI:10.1109/CCCAI59026.2023.00041