ML Anthology

RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth

Fabian Brand, Kristian Fischer, Alexander Kopte, Marc Windsheimer, André Kaup
CVPRW 2022 pp. 1758-1762
doi:10.1109/CVPRW56347.2022.00186 /cvprw/2022/brand2022cvprw-rdonet/

Abstract

Rate-distortion optimization (RDO) is responsible for large gains in image and video compression. While RDO is a standard tool in traditional image and video coding, it is not yet widely used in novel end-to-end trained neural methods. The major reason is that the decoding function is trained once and does not have free parameters. In this paper, we present RDONet, a network containing state-of-the-art components, which is perceptually optimized and capable of rate-distortion optimization. With this network, we are able to outperform VVC Intra on MS-SSIM and two different perceptual LPIPS metrics. This paper is part of the CLIC challenge, where we participate under the team name RDONet FAU.

CVPRW Semantic Scholar

Cite

Text

Brand et al. "RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00186

Markdown

[Brand et al. "RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/brand2022cvprw-rdonet/) doi:10.1109/CVPRW56347.2022.00186

BibTeX

@inproceedings{brand2022cvprw-rdonet,
  title     = {{RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth}},
  author    = {Brand, Fabian and Fischer, Kristian and Kopte, Alexander and Windsheimer, Marc and Kaup, André},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2022},
  pages     = {1758-1762},
  doi       = {10.1109/CVPRW56347.2022.00186},
  url       = {https://mlanthology.org/cvprw/2022/brand2022cvprw-rdonet/}
}