mlsanityData to 7 Oct 2026

Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain

arXiv 2402.17200 ↗First posted 27 Feb 2024cs.CV eess.IVcited by 1

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Abstract (arXiv)

Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain. This bias makes enhanced images closely resemble their compressed counterparts, thus degrading their perceptual quality. In this paper, we propose a simple yet effective method to mitigate this bias and enhance the quality of compressed images. Our method employs a conditional discriminator with the compressed image as a key condition, and then incorporates a domain-divergence regularization to actively distance the enhancement domain from the compression domain. Through this dual strategy, our method enables the discrimination against the compression domain, and brings the enhancement domain closer to the raw domain. Comprehensive quality evaluations confirm the superiority of our method over other state-of-the-art methods without incurring inference overheads.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 55 references that are in the map. First posted on arXiv 27 Feb 2024.

Builds on

35 of 55 references.

References · 35

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23 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Image Quality Assessment: Enhancing Perceptual Exploration and Interpretation with Collaborative Feature Refinement and Hausdorff distancecited by 0