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

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.

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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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