2019/09/30 by Hilmi E. Egilmez, Yung-Hsuan Chao, Yung‐Hsuan Chao +1
Computer Science · Engineering · Mathematics · #Advanced Data Compression Techniques #Algorithm #Artificial intelligence #Computer science #Data compression #Decoding methods #Discrete cosine transform #Gaussian #Image (mathematics) #Image compression #Image processing #Karhunen–Loève theorem #Mathematics #Transform coding #Video Analysis and Summarization #Video Coding and Compression Technologies #cs.LG #cs.MM #cs.SY #eess.IV #eess.SY #stat.AP #stat.ML
paper · pdf · doi:10.1109/tip.2020.3026627
To appear in IEEE Trans. on Image Processing (14 pages)
openalex publication_date 2020/01/01 · arxiv created 2020/09/18 · arxiv updated 2020/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest. This paper introduces a class of transforms called graph-based transforms (GBTs) for video compression, and proposes two different techniques to design GBTs. In the first technique, we formulate an optimization problem to learn graphs from data and provide solutions for optimal separable and nonseparable GBT designs, called GL-GBTs. The optimality of the proposed GL-GBTs is also theoretically analyzed based on Gaussian-Markov random field (GMRF) models for intra and inter predicted block signals. The second technique develops edge-adaptive GBTs (EA-GBTs) in order to flexibly adapt transforms to block signals with image edges (discontinuities). The advantages of EA-GBTs are both theoretically and empirically demonstrated. Our experimental results show that the proposed transforms can significantly outperform the traditional Karhunen-Loeve transform (KLT).