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HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform

arXiv 2305.07519 ↗First posted 12 May 2023cs.CV cs.MM eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 12 May 2023.

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References · 17

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5 more
  1. 2018Patch-Based Image Inpainting with Generative Adversarial Networkscited by 70
  2. 2021Perceptual Image Quality Assessment with Transformerscited by 54
  3. 2021YOLO5Face: Why Reinventing a Face Detectorcited by 44
  4. 2022PO-ELIC: Perception-Oriented Efficient Learned Image Codingcited by 35
  5. 2022Content-oriented learned image compressioncited by 12

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