Special Issue "Information Entropy Algorithms for Image, Video, and Signal Processing"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 1 April 2021.

Special Issue Editor

Dr. Gwanggil Jeon

Guest Editor
Department of Embedded Systems Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Korea
Interests: wireless communication; 5G; IoT; artificial intelligence; machine learning; data fusion learning; remote sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Information entropy is a basic concept in information theory associated with any random variable. Information entropy can be interpreted as the average level of information, surprise, and uncertainty inherent in a variable’s possible outcomes. The concept of information entropy was introduced by Claude Shannon in his 1948 paper, A Mathematical Theory of Communication. Over the last few years, entropy has become as an adequate trade-off measure in image, video, and signal processing. Especially, entropy measures have been used in the image, video, and signal processing area to cover the topics of Chroma subsampling, coding tree unit, color space, compression artifact, image resolution, and macroblock pixel. In addition, entropy measures have been also used in video processing to cover the topics of bit rate, display resolution, frame rate, interlaced video, and video quality. As the daily produced data is increasing rapidly, more effective applications to image, video, and signal processing are required. This Special Issue calls for recent studies on various image, video, and signal processing algorithms that are based on information entropy. Papers of both a theoretical and applicative nature are welcome, as well as contributions regarding new image and video processing techniques for the entropy research community. Major topics of interest include but are not restricted to the following:

Keywords:

  • Multichannel imaging;
  • Sensor size, channel number, and dynamic range;
  • Modeling of signal processing;
  • Compression approach;
  • Entropy-based video coding;
  • Prediction and redundancy for video coding;
  • Noise removal approach;
  • GPU-based methods for signal processing;
  • Signal quality assessment 

Prof. Dr. Gwanggil Jeon
Guest Editor

Manuscript Submission Information

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Published Papers (6 papers)

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Research

Open AccessArticle
Entropy Analysis of COVID-19 Cardiovascular Signals
Entropy 2021, 23(1), 87; https://doi.org/10.3390/e23010087 - 09 Jan 2021
Abstract
The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that [...] Read more.
The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that the complex mechanisms that change the status of ANS could only be solved by advanced multidimensional analysis of many variables, obtained both from the original cardiovascular signals and from laboratory analysis and detailed patient history. The aim of this paper is to analyze different measures of entropy as potential dimensions of the multidimensional space of cardiovascular data. The measures were applied to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy controls. Methods that indicate a statistically significant difference between patients with different levels of infection and healthy controls will be used for further multivariate research. As a result, it was shown that a statistically significant difference between healthy controls and patients with COVID-19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic dynamics entropy, and copula parameters. Statistical significance between serious and mild patients with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure. This result contributes to the hypothesis that the severity of COVID-19 disease is associated with ANS disorder and encourages further research. Full article
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Open AccessArticle
A New Hybrid Possibilistic-Probabilistic Decision-Making Scheme for Classification
Entropy 2021, 23(1), 67; https://doi.org/10.3390/e23010067 - 03 Jan 2021
Abstract
Uncertainty is at the heart of decision-making processes in most real-world applications. Uncertainty can be broadly categorized into two types: aleatory and epistemic. Aleatory uncertainty describes the variability in the physical system where sensors provide information (hard) of a probabilistic type. Epistemic uncertainty [...] Read more.
Uncertainty is at the heart of decision-making processes in most real-world applications. Uncertainty can be broadly categorized into two types: aleatory and epistemic. Aleatory uncertainty describes the variability in the physical system where sensors provide information (hard) of a probabilistic type. Epistemic uncertainty appears when the information is incomplete or vague such as judgments or human expert appreciations in linguistic form. Linguistic information (soft) typically introduces a possibilistic type of uncertainty. This paper is concerned with the problem of classification where the available information, concerning the observed features, may be of a probabilistic nature for some features, and of a possibilistic nature for some others. In this configuration, most encountered studies transform one of the two information types into the other form, and then apply either classical Bayesian-based or possibilistic-based decision-making criteria. In this paper, a new hybrid decision-making scheme is proposed for classification when hard and soft information sources are present. A new Possibilistic Maximum Likelihood (PML) criterion is introduced to improve classification rates compared to a classical approach using only information from hard sources. The proposed PML allows to jointly exploit both probabilistic and possibilistic sources within the same probabilistic decision-making framework, without imposing to convert the possibilistic sources into probabilistic ones, and vice versa. Full article
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Open AccessArticle
A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees
Entropy 2021, 23(1), 20; https://doi.org/10.3390/e23010020 - 25 Dec 2020
Abstract
At present, many Deep Neural Network (DNN) methods have been widely used for hyperspectral image classification. Promising classification results have been obtained by utilizing such models. However, due to the complexity and depth of the model, increasing the number of model parameters may [...] Read more.
At present, many Deep Neural Network (DNN) methods have been widely used for hyperspectral image classification. Promising classification results have been obtained by utilizing such models. However, due to the complexity and depth of the model, increasing the number of model parameters may lead to an overfitting of the model, especially when training data are insufficient. As the performance of the model mainly depends on sufficient data and a large network with reasonably optimized hyperparameters, using DNNs for classification requires better hardware conditions and sufficient training time. This paper proposes a feature fusion and multi-layered gradient boosting decision tree model (FF-DT) for hyperspectral image classification. First, we fuse extended morphology profiles (EMPs), linear multi-scale spatial characteristics, and nonlinear multi-scale spatial characteristics as final features to extract both special and spectral features. Furthermore, a multi-layered gradient boosting decision tree model is constructed for classification. We conduct experiments based on three datasets, which in this paper are referred to as the Pavia University, Indiana Pines, and Salinas datasets. It is shown that the proposed FF-DT achieves better performance in classification accuracy, training conditions, and time consumption than other current classical hyperspectral image classification methods. Full article
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Open AccessArticle
Modified Gerchberg–Saxton (G-S) Algorithm and Its Application
Entropy 2020, 22(12), 1354; https://doi.org/10.3390/e22121354 - 30 Nov 2020
Abstract
The Gerchberg–Saxton (G-S) algorithm is a phase retrieval algorithm that is widely used in beam shaping and optical information processing. However, the G-S algorithm has difficulty obtaining the exact solution after iterating, and an approximate solution is often obtained. In this paper, we [...] Read more.
The Gerchberg–Saxton (G-S) algorithm is a phase retrieval algorithm that is widely used in beam shaping and optical information processing. However, the G-S algorithm has difficulty obtaining the exact solution after iterating, and an approximate solution is often obtained. In this paper, we propose a series of modified G-S algorithms based on the Fresnel transform domain, including the single-phase retrieval (SPR) algorithm, the double-phase retrieval (DPR) algorithm, and the multiple-phase retrieval (MPR) algorithm. The analysis results show that the convergence of the SPR algorithm is better than that of the G-S algorithm, but the exact solution is not obtained. The DPR and MPR algorithms have good convergence and can obtain exact solutions; that is, the information is recovered losslessly. We discuss the security advantages and verification reliability of the proposed algorithms in image encryption. A multiple-image encryption scheme is proposed, in which n plaintexts can be recovered from n ciphertexts, which greatly improves the efficiency of the system. Finally, the proposed algorithms are compared with the current phase retrieval algorithms, and future applications are discussed. We hope that our research can provide new ideas for the application of the G-S algorithm. Full article
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Open AccessArticle
A Chaotic-Based Encryption/Decryption Framework for Secure Multimedia Communications
Entropy 2020, 22(11), 1253; https://doi.org/10.3390/e22111253 - 04 Nov 2020
Cited by 1
Abstract
Chaos-based encryption has shown an increasingly important and dominant role in modern multimedia cryptography compared with traditional algorithms. This work proposes novel chaotic-based multimedia encryption schemes utilizing 2D alteration models for high secure data transmission. A novel perturbation-based data encryption for both confusion [...] Read more.
Chaos-based encryption has shown an increasingly important and dominant role in modern multimedia cryptography compared with traditional algorithms. This work proposes novel chaotic-based multimedia encryption schemes utilizing 2D alteration models for high secure data transmission. A novel perturbation-based data encryption for both confusion and diffusion rounds is proposed. Our chaotification structure is hybrid, in which multiple maps are combined combines for media encryption. Blended chaotic maps are used to generate the control parameters for the permutation (shuffling) and diffusion (substitution) structures. The proposed schemes not only maintain great encryption quality reproduced by chaotic, but also possess other advantages, including key sensitivity and low residual clarity. Extensive security and differential analyses documented that the proposed schemes are efficient for secure multimedia transmission as well as the encrypted media possesses resistance to attacks. Additionally, statistical evaluations using well-known metrics for specific media types, show that proposed encryption schemes can acquire low residual intelligibility with excessive nice recovered statistics. Finally, the advantages of the proposed schemes have been highlighted by comparing it against different state-of-the-art algorithms from literature. The comparative performance results documented that our schemes are extra efficacious than their data-specific counterpart methods. Full article
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Open AccessArticle
An Image-Based Class Retrieval System for Roman Republican Coins
Entropy 2020, 22(8), 799; https://doi.org/10.3390/e22080799 - 22 Jul 2020
Abstract
We propose an image-based class retrieval system for ancient Roman Republican coins that can be instrumental in various archaeological applications such as museums, Numismatics study, and even online auctions websites. For such applications, the aim is not only classification of a given coin, [...] Read more.
We propose an image-based class retrieval system for ancient Roman Republican coins that can be instrumental in various archaeological applications such as museums, Numismatics study, and even online auctions websites. For such applications, the aim is not only classification of a given coin, but also the retrieval of its information from standard reference book. Such classification and information retrieval is performed by our proposed system via a user friendly graphical user interface (GUI). The query coin image gets matched with exemplar images of each coin class stored in the database. The retrieved coin classes are then displayed in the GUI along with their descriptions from a reference book. However, it is highly impractical to match a query image with each of the class exemplar images as there are 10 exemplar images for each of the 60 coin classes. Similarly, displaying all the retrieved coin classes and their respective information in the GUI will cause user inconvenience. Consequently, to avoid such brute-force matching, we incrementally vary the number of matches per class to find the least matches attaining the maximum classification accuracy. In a similar manner, we also extend the search space for coin class to find the minimal number of retrieved classes that achieve maximum classification accuracy. On the current dataset, our system successfully attains a classification accuracy of 99% for five matches per class such that the top ten retrieved classes are considered. As a result, the computational complexity is reduced by matching the query image with only half of the exemplar images per class. In addition, displaying the top 10 retrieved classes is far more convenient than displaying all 60 classes. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Evaluation of entropy-based uncertainty measures within the framework of the evidence theory
Authors: Samia Barhoumi 1, Imene Khanfir Kallel 1,2, Sonda Ammar Bouhamed 1,2, Éloi Bossé 2,3, Basel Solaiman 2
Affiliation:

1. Control and Energy Management Lab, Cybernics Team, ENIS/ISBS, University of Sfax, Tunisia
2. Image & Information Processing Department (iTi), IMT-Atlantique, Technopôle Brest Iroise CS 83818, 29238 Brest, France
3. Expertises Parafuse Inc. (Analytics-Information Fusion, Decision Support), 1006 Blvd Pie XII,Québec, QC, G1W 4N1, Québec, Canada

 

Title: Multimedia Security Algorithms Based on Chaotic System for Fog Computing
Authors: Ibrahim Yasser, Mohamed A. Mohamed, Ahmed S. Samra, and Fahmi Khalifa
Affiliation: Mansoura University, Mansoura 35516, Egypt

 

Title: A Secured Fragile Watermarking Scheme for Image Tamper Localization and Self-Recovery
Authors: Mohamed A. Mohamed, Mohamed G. Abdel-Fattah, and Fahmi Khalifa
Affiliation: Mansoura University, Mansoura 35516, Egypt

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