FAST LEARNING-BASED SPLIT TYPE PREDICTION ALGORITHM FOR VVC

Date

2023-10

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding com- plexity, which seriously hinders its widespread application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split type (ST) prediction algorithm for VVC using a deep learning approach. We first construct a large-scale database containing sufficient STs with diverse video resolution and content. Next, since the ST distributions of coding units (CUs) of different sizes are significantly distinct, so we separately design neural net- works for all different CU sizes. Then, we merge ambiguous STs into four merged classes (MCs) to train models to obtain probabilities of MCs and skip unlikely ones. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 67.53% with 1.89% increase in Bjøntegaard delta bit-rate (BDBR) on average.

Description

Keywords

VVC, split type, deep learning

Citation

Wang, D., Chen, L., Lu, X., Dufaux, F., Li, W. and Zhu, C. (2023) FAST LEARNING-BASED SPLIT TYPE PREDICTION ALGORITHM FOR VVC. IEEE International Conference on Image Processing (ICIP)

Rights

Research Institute

Institute of Artificial Intelligence (IAI)