HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform

• URL: http://arxiv.org/abs/2305.07519v4
• Date: Thu, 18 May 2023 08:52:49 GMT
• Title: HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform
• Authors: Peirong Ning, Wei Jiang, Ronggang Wang
• Abstract summary: Current learning-based image compression methods often sacrifice human-friendly compression and require long decoding times. We propose enhancements to the backbone network and loss function of existing image compression model, focusing on improving human perception and efficiency.
• Score: 16.173583505483272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been rapid development in learned image compression techniques that prioritize ratedistortion-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 at the same subjective quality.

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