Blind image quality assessment on authentically distorted images with perceptual features

Almost all present successful general purpose blind image quality assessment (BIQA) models are designed only towards singly and synthetically distorted images. However, real world images generally contain multiply types of distortions other than single type of distortion. Even some state of the art...

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Bibliographic Details
Main Authors: Luping Yang, Haiqing Du, Jingtao Xu, Yong Liu
Format: Conference Proceeding
Language:English
Subjects:
authentic distortions
blind image quality assessment
Decision support systems
Hafnium
human visual system
Natural scene statistics
Online Access:Request full text
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Description
Summary:Almost all present successful general purpose blind image quality assessment (BIQA) models are designed only towards singly and synthetically distorted images. However, real world images generally contain multiply types of distortions other than single type of distortion. Even some state of the art BIQA methods cannot work well on authentically distorted images. In this paper we propose a novel effective BIQA method for authentically distorted images to remedy the shortage of the popular BIQA models. First, except for traditional natural scene statistics (NSS) based features, we introduce a series of quality aware features based on human visual system (HVS), such as image dynamic range, color information, blurriness. Then support vector regression (SVR) is utilized to learn the mapping between the combined effective features and human opinion scores. Experimental results demonstrate the promising performance of the proposed method.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7532717