The impact of the entropy parameter in generalized information measures for image quality assessment

We introduce and study Mathai’s cumulative residual entropy, a generalized entropy measure motivated by Mathai’s entropy. We establish fundamental properties, including finiteness, limiting behavior, and connections with existing cumulative residual entropy measures. The entropy parameter q is highlighted as a flexible mechanism for controlling sensitivity to distributional and structural features. The practical applicability of the proposed measure is illustrated through texture analysis and image quality assessment framework. Experimental results show that the entropy parameter q effectively captures variations in image degradation and structural characteristics. These findings show that the proposed Mathai’s cumulative residual entropy measure, compared with its Tsallis counterpart, provides a flexible and interpretable approach to texture analysis and blind image quality assessment.

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Publication Details

Journal
Hacettepe Journal of Mathematics and Statistics
Published
2026-10-03
DOI
https://doi.org/10.15672/hujms.1868733
Primary Topic
Image and Video Quality Assessment
Type
article
Field-Weighted Citation Impact
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article

The impact of the entropy parameter in generalized information measures for image quality assessment

Nicy Sebastian, T. Princy
Hacettepe Journal of Mathematics and Statistics
Image and Video Quality Assessment
article

The impact of the entropy parameter in generalized information measures for image quality assessment

Nicy Sebastian, T. Princy
article en

Abstract

We introduce and study Mathai’s cumulative residual entropy, a generalized entropy measure motivated by Mathai’s entropy. We establish fundamental properties, including finiteness, limiting behavior, and connections with existing cumulative residual entropy measures. The entropy parameter q is highlighted as a flexible mechanism for controlling sensitivity to distributional and structural features. The practical applicability of the proposed measure is illustrated through texture analysis and image quality assessment framework. Experimental results show that the entropy parameter q effectively captures variations in image degradation and structural characteristics. These findings show that the proposed Mathai’s cumulative residual entropy measure, compared with its Tsallis counterpart, provides a flexible and interpretable approach to texture analysis and blind image quality assessment.

Hacettepe Journal of Mathematics and Statistics(Advanced Online Publication)
Cochin University of Science and Technology (IN), Thomas College (US)
Openalex Percentile: Top 14%
Image and Video Quality Assessment
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