No-reference image quality assessment for contrastdistorted images using statistical features in Curvelet domain
Most No-Reference Image Quality Assessment (NR-IQA) metrics are designed for the quality assessment of images distorted by compression, noise and blurring. Few NR-IQA metrics exist for Contrast-Distorted Images (CDI).Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-I...
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Asian Research Publishing Network
2023
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| Summary: | Most No-Reference Image Quality Assessment (NR-IQA) metrics are designed for the quality assessment of images distorted by compression, noise and blurring. Few NR-IQA metrics exist for Contrast-Distorted Images (CDI).Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast- Distorted Images (NR-IQACDI) are the state-of-the-art IQA algorithms for CDI. Room for improvement exists, especially for the assessment results using the image database called TID2013. The current NR-IQACDI uses features in spatial domain. This paper proposes the use of the same statistical features but in Curvelet domain, which is powerful in capturing the multiscale and multidirectional information of an image. Experiments are conducted to assess the effect of using statistical features in Curvelet domain. The experiment results are based on K-fold cross validation with K range from (2 to 10).The statistical tests indicate that the performance using selected statistical features in the Curvelet domain are better than that of the NRIQACDI. The use of other statistical features and selection methods should be further investigated to increase the prediction performance. � 2006-2017 Asian Research Publishing Network (ARPN). All rights reserved. |
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