Versatile Recompression-Aware Perceptual Image Super-Resolution

Mingwei He, Tongda Xu, Xingtong Ge, Ming Sun, Chao Zhou +1 more
11/22/2025
cs.CV

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Abstract

Perceptual image super-resolution (SR) methods restore degraded images and produce sharp outputs. In practice, those outputs are usually recompressed for storage and transmission. Ignoring recompression is suboptimal as the downstream codec might add additional artifacts to restored images. However, jointly optimizing SR and recompression is challenging, as the codecs are not differentiable and vary in configuration. In this paper, we present Versatile Recompression-Aware Perceptual Super-Resolution (VRPSR), which makes existing perceptual SR aware of versatile compression. First, we formulate compression as conditional text-to-image generation and utilize a pre-trained diffusion model to build a generalizable codec simulator. Next, we propose a set of training techniques tailored for perceptual SR, including optimizing the simulator using perceptual targets and adopting slightly compressed images as the training target. Empirically, our VRPSR saves more than 10\% bitrate based on Real-ESRGAN and S3Diff under H.264/H.265/H.266 compression. Besides, our VRPSR facilitates joint optimization of the SR and post-processing model after recompression.

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Cite this paper

@article{he2025versatile,
  title  = {Versatile Recompression-Aware Perceptual Image Super-Resolution},
  author = {Mingwei He and Tongda Xu and Xingtong Ge and Ming Sun and Chao Zhou and Yan Wang},
  year   = {2025},
  eprint = {2511.18090},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.18090v1}
}

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