Fast CU Partition Decision Strategy Based on Human Visual System Perceptual Quality

Jinchao Zhao,Qiuwen Zhang,Tengyao Cui

doi:10.1109/access.2021.3110292

Jinchao Zhao, Qiuwen Zhang + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3110292

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Abstract

A fast Coding Unit (CU) partition decision strategy based on Human Visual System (HVS) perception quality is proposed in this paper. Considering that it is difficult for existing fast algorithms to further improve compression efficiency, perceptual coding technology has been tried to remove visual redundancy for achieving the purpose of reducing the bit rate on the basis of maintaining subjective visual quality. However, the existing perceptual coding model is still insufficient to reflect the characteristics of the HVS, which has limited improvement in coding efficiency, especially in fast algorithms. In this method, the characteristics of the limited human capacity for spatial-temporal resolution and the visual sensory memory are used to improve coding performance. First, the color complexity is used as a control factor to optimize the Just Noticeable Difference (JND) model to remove visual redundancy in a way that is more in line with the characteristics of visual perception. Second, a classification model of motion patterns based on human visual saliency is designed to provide a basis for CU classification, which effectively improves the coding accuracy. Finally, an offline Decision Tree (DT) classifier is designed based on the above model, and texture features are incorporated into the classifier as another key attribute to further reduce the computational complexity. The results of performance evaluation confirm that the proposed method achieves significantly improved coding performance compared with original Versatile Test Model (VTM). Compared with existing algorithms, our method not only improves coding efficiency, but also improves subjective visual quality.

Highlights

With the vigorous development of multimedia, video services with high visual quality and low transmission cost are urgently needed in many fields
In order to promote the development of video coding technology, the Joint Video Exploration Team (JVET) composed by the Video Coding Experts Group (VCEG) and the Moving Picture Experts Group (MPEG) released a new generation of video coding technology standard, H.266/Versatile Video Coding (VVC) [1], which provides a new platform for the development of video technology
We use the Just Noticeable Difference (JND) model and the motion state as classification attributes, and combine them with the decision tree to develop a Coding Unit (CU) partition decision strategy oriented to the perceived quality of Human Visual System (HVS)

Summary

Introduction

With the vigorous development of multimedia, video services with high visual quality and low transmission cost are urgently needed in many fields. A series of new coding tools, such as the Quad Tree with nested Multi Type Tree (QTMT) partition structure, are introduced into VVC [2]. The Coding Unit (CU) has obtained the asymmetry and directionality due to the introduction of QTMT. These technical improvements have brought nearly half of the compression gain to VVC with the expense of obviously increased computational complexity. It is still challenging to significantly reducing the computational cost of finding the best CU partition structure. It is necessary to find a coding scheme with low computational complexity and high efficiency

Methods

Results

Conclusion