Graph-based Transforms for Video Coding

TL;DR

This paper introduces graph-based transforms for video compression, including data-driven and edge-adaptive methods, which outperform traditional transforms like KLT in efficiency and adaptability.

Contribution

It proposes two novel graph-based transform techniques for video coding, with theoretical analysis and empirical validation showing superior performance over traditional methods.

Findings

GL-GBTs are optimally designed and theoretically analyzed.

EA-GBTs adapt to image edges, improving transform flexibility.

Proposed transforms outperform KLT in experiments.

Abstract

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 demonstrate that the proposed transforms can significantly outperform the traditional Karhunen-Loeve transform (KLT).