Enhanced Machine Learning-based Inter Coding for VVC

被引：4

作者：

Benjak, Martin ^{[1
]}

Meuel, Holger ^{[1
]}

Laude, Thorsten ^{[1
]}

Ostermann, Jorn ^{[1
]}

机构：

[1] Leibniz Univ Hannover, Inst Informat Verarbeitung, Hannover, Germany

来源：

3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (IEEE ICAIIC 2021) | 2021年

关键词：

VVC; inter coding; video coding; machine learning; recurrent neural networks;

D O I：

10.1109/ICAIIC51459.2021.9415184

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose an enhanced machine learning-based inter coding algorithm for VVC. Conceptually, the reference pictures from the decoded picture buffer are processed using a recurrent neural network to generate an artificial reference picture at the time instance of the currently coded picture. The network is trained using a SATD cost function to minimize the bit rate cost for the prediction error rather than the pixel-wise difference. By this we achieved average weighted BD-rate gains of 0.94%. The coding time increased about 5% for the encoder and 300% for the decoder due to the use of a neural network.

引用

页码：21 / 25

页数：5

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