Towards the Perceptual Quality Enhancement of Low Bit-Rate Compressed Images

Younhee Kim, Seunghyun Cho, Jooyoung Lee, Seyoon Jeong, Jin Soo Choi, Jihoon Do

CVPRW 2020 pp. 565-569

doi:10.1109/CVPRW50498.2020.00076 /cvprw/2020/kim2020cvprw-perceptual/

Abstract

In this paper, a low bit-rate compressed image quality enhancement framework is presented. A recent image/video coding method and a deep learning based quality enhancement method are integrated to improve the perceptual quality of compressed images. The proposed architecture is designed to reduce the coding artifact and restore the blurred texture details. The experimental results presents that the proposed framework yields a 33% improvement in the Perceptual Index score which is consistent with visual evaluation on a sample of results.

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Cite

Text

Kim et al. "Towards the Perceptual Quality Enhancement of Low Bit-Rate Compressed Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00076

Markdown

[Kim et al. "Towards the Perceptual Quality Enhancement of Low Bit-Rate Compressed Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/kim2020cvprw-perceptual/) doi:10.1109/CVPRW50498.2020.00076

BibTeX

@inproceedings{kim2020cvprw-perceptual,
  title     = {{Towards the Perceptual Quality Enhancement of Low Bit-Rate Compressed Images}},
  author    = {Kim, Younhee and Cho, Seunghyun and Lee, Jooyoung and Jeong, Seyoon and Choi, Jin Soo and Do, Jihoon},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {565-569},
  doi       = {10.1109/CVPRW50498.2020.00076},
  url       = {https://mlanthology.org/cvprw/2020/kim2020cvprw-perceptual/}
}