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

Brand F, Fischer K, Kopte A, Windsheimer M, Kaup A (2022)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Pages Range: 1759-1763

Event location: New Orleans US

URI: https://ieeexplore.ieee.org/document/9857403

DOI: 10.1109/CVPRW56347.2022.00186

Open Access Link: https://openaccess.thecvf.com/content/CVPR2022W/CLIC/html/Brand_RDONet_Rate-Distortion_Optimized_Learned_Image_Compression_With_Variable_Depth_CVPRW_2022_paper.html

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.

Authors with CRIS profile

Fabian Brand Lehrstuhl für Multimediakommunikation und Signalverarbeitung Kristian Fischer Lehrstuhl für Multimediakommunikation und Signalverarbeitung Alexander Kopte Lehrstuhl für Multimediakommunikation und Signalverarbeitung Marc Windsheimer Lehrstuhl für Multimediakommunikation und Signalverarbeitung Andre Kaup Lehrstuhl für Multimediakommunikation und Signalverarbeitung

How to cite

APA:

Brand, F., Fischer, K., Kopte, A., Windsheimer, M., & Kaup, A. (2022). RDONet: Rate-Distortion Optimized Learned Image Compression With Variable Depth. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (pp. 1759-1763). New Orleans, US.

MLA:

Brand, Fabian, et al. "RDONet: Rate-Distortion Optimized Learned Image Compression With Variable Depth." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, New Orleans 2022. 1759-1763.

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