Brand F, Fischer K, Kopte A, Windsheimer M, Kaup A (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: IEEE Computer Society
Book Volume: 2022-June
Pages Range: 1758-1762
Conference Proceedings Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Event location: New Orleans, LA, USA
ISBN: 9781665487399
DOI: 10.1109/CVPRW56347.2022.00186
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.
APA:
Brand, F., Fischer, K., Kopte, A., Windsheimer, M., & Kaup, A. (2022). RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 1758-1762). New Orleans, LA, USA: IEEE Computer Society.
MLA:
Brand, Fabian, et al. "RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022, New Orleans, LA, USA IEEE Computer Society, 2022. 1758-1762.
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