2 research outputs found
Explainable Machine Learning based Transform Coding for High Efficiency Intra Prediction
Machine learning techniques provide a chance to explore the coding
performance potential of transform. In this work, we propose an explainable
transform based intra video coding to improve the coding efficiency. Firstly,
we model machine learning based transform design as an optimization problem of
maximizing the energy compaction or decorrelation capability. The explainable
machine learning based transform, i.e., Subspace Approximation with Adjusted
Bias (Saab) transform, is analyzed and compared with the mainstream Discrete
Cosine Transform (DCT) on their energy compaction and decorrelation
capabilities. Secondly, we propose a Saab transform based intra video coding
framework with off-line Saab transform learning. Meanwhile, intra mode
dependent Saab transform is developed. Then, Rate Distortion (RD) gain of Saab
transform based intra video coding is theoretically and experimentally analyzed
in detail. Finally, three strategies on integrating the Saab transform and DCT
in intra video coding are developed to improve the coding efficiency.
Experimental results demonstrate that the proposed 88 Saab transform
based intra video coding can achieve Bj{\o}nteggard Delta Bit Rate (BDBR) from
-1.19% to -10.00% and -3.07% on average as compared with the mainstream
88 DCT based coding scheme.Comment: 13 pages, 9 figure
Graph-based Transforms for Video Coding
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).Comment: To appear in IEEE Trans. on Image Processing (14 pages