Graph-Based Transforms for Video Coding

doi:10.1109/TIP.2020.3026627

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Graph-Based Transforms for Video Coding

2020-01-01

Hilmi E. Egilmez *

Yung‐Hsuan Chao

Antonio Ortega

DOI：10.1109/TIP.2020.3026627

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Abstract

En 中文

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).

Keywords:

Transforms

Image coding

Laplace equations

Image edge detection

Covariance matrices

Symmetric matrices

Video coding

Transform coding

predictive coding

graph-based transforms

video coding

compression

optimization

statistical modeling

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