HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform
This work addresses human perception and efficiency in image compression, but it is incremental as it builds on existing models.
The paper tackled the problem of learned image compression methods sacrificing human-friendly compression and requiring long decoding times, by proposing enhancements to the backbone network and loss function, resulting in more than 25% bit-rate saving at the same subjective quality.
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.