A No-Reference Image and Video Visual Quality Metric Based on Machine Learning

被引：0

作者：

Frantc, Vladimir ^{[1
]}

Voronin, Viacheslav ^{[1
]}

Semenishchev, Evgenii ^{[1
]}

Minkin, Maxim ^{[1
]}

Delov, Aliy ^{[2
]}

机构：

[1] Don State Tech Univ, Rostov Na Donu, Russia

[2] SC Sci & Innovat, Moscow, Russia

来源：

TENTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2017) | 2018年 / 10696卷

关键词：

Lossy compression; visual quality; machine learning;

D O I：

10.1117/12.2309517

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper presents a novel visual quality metric for lossy compressed video quality assessment. High degree of correlation with subjective estimations of quality is due to using of a convolutional neural network trained on a large amount of pairs video sequence-subjective quality score. We demonstrate how our predicted no-reference quality metric correlates with qualitative opinion in a human observer study. Results are shown on the EVVQ dataset with comparison existing approaches.

引用

页数：5

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