Blind DIBR-Synthesized Image Quality Assessment based on Sparsity Features in Morphological Multiscale Domain

被引：0
|
作者：
Bokan, Dejan [1 ]
Velikic, Gordana [1 ]
Kukolj, Dragan [2 ]
Sandic-Stankovic, Dragana [3 ]
机构：
[1] RT RK Inst Comp Based Syst, Novi Sad, Serbia
[2] Univ Novi Sad, Fac Tech Sci, Novi Sad, Serbia
[3] IRITEL, Inst Telecommun & Elect, Belgrade, Serbia
来源：
2017 NINTH INTERNATIONAL CONFERENCE ON QUALITY OF MULTIMEDIA EXPERIENCE (QOMEX) | 2017年
关键词：
No-reference synthisized image quality metric; Morphological wavelet decomposition; Morphological pyramid; Sparsity features; General regression neural network;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper a no-reference image quality assessment (IQA) metric for DIBR-synthesized images is proposed. Sparsity based features of morphologically decomposed image subbands are used to estimate distortion level in images. A General regression neural network is utilized to calculate quality score. The performance is evaluated using publicly available IRCCyN/IVC DIBR image database. Experimental results show that proposed metric accords with human subjective judgment.
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页数：3
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