Learning True Rate-Distortion-Optimization for End-To-End Image Compression

Autor:	Brand, Fabian, Fischer, Kristian, Kopte, Alexander, Kaup, André
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Even though rate-distortion optimization is a crucial part of traditional image and video compression, not many approaches exist which transfer this concept to end-to-end-trained image compression. Most frameworks contain static compression and decompression models which are fixed after training, so efficient rate-distortion optimization is not possible. In a previous work, we proposed RDONet, which enables an RDO approach comparable to adaptive block partitioning in HEVC. In this paper, we enhance the training by introducing low-complexity estimations of the RDO result into the training. Additionally, we propose fast and very fast RDO inference modes. With our novel training method, we achieve average rate savings of 19.6% in MS-SSIM over the previous RDONet model, which equals rate savings of 27.3% over a comparable conventional deep image coder. Comment: Accepted to DCC as Poster
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.01586 Zobrazit plný text záznamu View this record from Arxiv