New feature selection algorithms for no-reference image quality assessment

No reference image quality assessment (NR-IQA) is a challenging task since reference images are usually unavailable in real world scenarios. The performance of NR-IQA techniques is vastly dependent on the features utilized to predict the image quality. Many NR-IQA techniques have been proposed that...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2018-10, Vol.48 (10), p.3482-3501
Main Authors: Nizami, Imran Fareed, Majid, Muhammad, Khurshid, Khawar
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Correlation
Discrete cosine transform
Feature extraction
Feature selection
Image quality
Machines
Manufacturing
Mechanical Engineering
Performance enhancement
Processes
Production planning
Quality
Quality assessment
Support vector machines
Wavelet transforms
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Summary:No reference image quality assessment (NR-IQA) is a challenging task since reference images are usually unavailable in real world scenarios. The performance of NR-IQA techniques is vastly dependent on the features utilized to predict the image quality. Many NR-IQA techniques have been proposed that extract features in different domains like spatial, discrete cosine transform and wavelet transform. These NR-IQA techniques have the possibility to contain redundant features, which result in degradation of quality score prediction. Recently impact of general purpose feature selection algorithms on NR-IQA techniques has shown promising results. But these feature selection algorithms have the tendency to select irrelevant features and discard relevant features. This paper presents fifteen new feature selection algorithms specifically designed for NR-IQA, which are based on Spearman rank ordered correlation constant (SROCC), linear correlation constant (LCC), Kendall correlation constant (KCC) and root mean squared error (RMSE). The proposed feature selection algorithms are applied on the extracted features of existing NR-IQA techniques. Support vector regression (SVR) is then applied to selected features to predict the image quality score. The fifteen newly proposed feature selection algorithms are evaluated using eight different NR-IQA techniques over three commonly used image quality assessment databases. Experimental results show that the proposed feature selection algorithms not only reduce the number of features but also improve the performance of NR-IQA techniques. Moreover, features selection algorithms based on SROCC and its combination with LCC, KCC and RMSE perform better in comparison to other proposed algorithms.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-018-1151-0