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Title: Thermodynamics-based evaluation of various improved Shannon entropies for configurational information of gray-level images
Authors: Gao, P 
Li, Z 
Zhang, H
Issue Date: 2018
Source: Entropy, Jan. 2018, v. 20, no. 1, 19, p. 1-25
Abstract: The quality of an image affects its utility and image quality assessment has been a hot research topic for many years. One widely used measure for image quality assessment is Shannon entropy, which has a well-established information-theoretic basis. The value of this entropy can be interpreted as the amount of information. However, Shannon entropy is badly adapted to information measurement in images, because it captures only the compositional information of an image and ignores the configurational aspect. To fix this problem, improved Shannon entropies have been actively proposed in the last few decades, but a thorough evaluation of their performance is still lacking. This study presents such an evaluation, involving twenty-three improved Shannon entropies based on various tools such as gray-level co-occurrence matrices and local binary patterns. For the evaluation, we proposed: (a) a strategy to generate testing (gray-level) images by simulating the mixing of ideal gases in thermodynamics; (b) three criteria consisting of validity, reliability, and ability to capture configurational disorder; and (c) three measures to assess the fulfillment of each criterion. The evaluation results show only the improved entropies based on local binary patterns are invalid for use in quantifying the configurational information of images, and the best variant of Shannon entropy in terms of reliability and ability is the one based on the average distance between same/different-value pixels. These conclusions are theoretically important in setting a direction for the future research on improving entropy and are practically useful in selecting an effective entropy for various image processing applications.
Keywords: Configurational information
Information content
Information entropy
Shannon entropy
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Entropy 
EISSN: 1099-4300
DOI: 10.3390/e20010019
Rights: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Gao, P., Li, Z., & Zhang, H. (2018). Thermodynamics-based evaluation of various improved Shannon entropies for configurational information of gray-level images. Entropy, 20(1), (Suppl. ), 19, - is available at
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