Machine-learning based Blind Visual Quality Assessment with Content-aware Data Partitioning

被引：0

作者：

Gavrovska, Ana M. ^{[1
]}

Zajic, Goran J. ^{[2
]}

Milivojevic, Milan S. ^{[1
]}

Reljin, Irini S. ^{[1
]}

机构：

[1] Univ Belgrade, Sch Elect Engn, Bulevar Kralja Aleksandra 73, Belgrade 11120, Serbia

[2] ICT Coll Vocat Studies, Zdravka Celara 16, Belgrade 11000, Serbia

来源：

2018 14TH SYMPOSIUM ON NEURAL NETWORKS AND APPLICATIONS (NEUREL) | 2018年

关键词：

Image quality assessment; multimedia; Quality of Experience; machine learning; data partitioning;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Over the years different machine-learning based image quality assessment models have been proposed. In this paper, we analyze data partitioning. Since statistical data partitioning may affect the results due to the number of iterations, we analyze the effect of content-aware partitioning. The results are analyzed for different partitioning methods and models using publicly available dataset and difference mean opinion scores.

引用

页数：5

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