Paper Info
Title
Distortion-specific feature selection algorithm for universal blind image quality assessment.
Abstract
Blind image quality assessment (BIQA) aims to use objective measures for predicting the quality score of distorted images without any prior information regarding the reference image. Several BIQA techniques are proposed in literature that use a two-step approach, i.e., feature extraction for distortion classification and regression for predicting the quality score. In this paper, a three-step approach is proposed that aims to improve the performance of BIQA techniques. In the first step, feature extraction is performed using existing BIQA techniques to determine the distortion type. Secondly, features are selected for each distortion type based on the mean value of Spearman rank ordered correlation constant (SROCC) and linear correlation constant (LCC). Lastly, distortion-specific features are used by regression model to predict the quality score. Experimental results show that the predicted quality score using distortion-specific features strongly correlates with the subjective quality score, improves the overall performance of existing BIQA techniques, and reduces the processing time.
Year
DOI
Venue
2019
10.1186/s13640-018-0392-5
EURASIP Journal on Image and Video Processing
Keywords
Field
DocType
Blind image quality assessment, Feature extraction, Feature selection, Classification, Support vector regression
Quality Score,Pattern recognition,Feature selection,Regression analysis,Computer science,Support vector machine,Image quality,Feature extraction,Artificial intelligence,Spearman's rank correlation coefficient,Distortion
Journal
Volume
Issue
ISSN
2019
1
1687-5281
Citations 
PageRank 
References 
1
0.35
38
Authors
5
Name
Order
Citations
PageRank
Imran Fareed Nizami1103.52
Muhammad Majid213118.32
Waleed Manzoor310.35
Khawar Khurshid483.84
Byeungwoo Jeon51045114.72