Texture based adaptive computational resource allocation for fast AVS3 inter coding

Abstract

The newest Audio Video Coding Standard (AVS3) generation provides better coding efficiency than its predecessor, where two new partitioning structures, i.e., Extend Quad-Tree (EQT) and Binary-Tree (BT), are adopted. Although these split tools bring remarkable coding performance, for the price of increasing of computational coding complexity. For the popular conference video applications, experiments show that the EQT or BT split times in different regions are quite different, which indicates that it is unnecessary to provide all partitioning candidate modes in different area. In this work, an effective partitioning resource allocation method is proposed to reduce computational complexity while guaranteeing the coding performance. Specifically, a Decision Tree (DT) model is trained to determine available partitioning modes for current Coding Unit (CU), where input features are the histogram, sobel texture and average residual difference between current and reference CU, along with the size of CU. The training data are selected from different test sequences of AVS and Joint Video Experts Team Common Test Conditions (JCT) sequences, which are identified by the Structural Similarity (SSIM). The experiments on 720p and Common Intermediate Format (CIF) sequences, implemented on platform of AVS3 reference software HPM-9.1, under Low Delay B (LB) configuration, show the efficiency of the proposed method, which can achieve more than 40.0% computational complexity reduction, and BDBR loss is less than 2.0%.