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: closed (1 April 2021).

Special Issue Editor

Dr. Gwanggil Jeon
E-Mail Website
Guest Editor
Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea
Interests: image processing; particularly image compression, motion estimation, demosaicking and image enhancement, and computational intelligence, such as fuzzy and rough sets theories
Special Issues, Collections and Topics 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 (10 papers)

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Editorial

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Editorial
Information Entropy Algorithms for Image, Video, and Signal Processing
Entropy 2021, 23(8), 926; https://0-doi-org.brum.beds.ac.uk/10.3390/e23080926 - 21 Jul 2021
Viewed by 473
Abstract
Information entropy is a basic concept in information theory associated with any random variable [...] Full article

Research

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Article
Improved Approach for the Maximum Entropy Deconvolution Problem
Entropy 2021, 23(5), 547; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050547 - 28 Apr 2021
Cited by 1 | Viewed by 508
Abstract
The probability density function (pdf) valid for the Gaussian case is often applied for describing the convolutional noise pdf in the blind adaptive deconvolution problem, although it is known that it can be applied only at the latter stages of the deconvolution process, [...] Read more.
The probability density function (pdf) valid for the Gaussian case is often applied for describing the convolutional noise pdf in the blind adaptive deconvolution problem, although it is known that it can be applied only at the latter stages of the deconvolution process, where the convolutional noise pdf tends to be approximately Gaussian. Recently, the deconvolutional noise pdf was approximated with the Edgeworth Expansion and with the Maximum Entropy density function for the 16 Quadrature Amplitude Modulation (QAM) input but no equalization performance improvement was seen for the hard channel case with the equalization algorithm based on the Maximum Entropy density function approach for the convolutional noise pdf compared with the original Maximum Entropy algorithm, while for the Edgeworth Expansion approximation technique, additional predefined parameters were needed in the algorithm. In this paper, the Generalized Gaussian density (GGD) function and the Edgeworth Expansion are applied for approximating the convolutional noise pdf for the 16 QAM input case, with no need for additional predefined parameters in the obtained equalization method. Simulation results indicate that improved equalization performance is obtained from the convergence time point of view of approximately 15,000 symbols for the hard channel case with our new proposed equalization method based on the new model for the convolutional noise pdf compared to the original Maximum Entropy algorithm. By convergence time, we mean the number of symbols required to reach a residual inter-symbol-interference (ISI) for which reliable decisions can be made on the equalized output sequence. Full article
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Article
Novel Features for Binary Time Series Based on Branch Length Similarity Entropy
Entropy 2021, 23(4), 480; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040480 - 18 Apr 2021
Cited by 1 | Viewed by 743
Abstract
Branch length similarity (BLS) entropy is defined in a network consisting of a single node and branches. In this study, we mapped the binary time-series signal to the circumference of the time circle so that the BLS entropy can be calculated for the [...] Read more.
Branch length similarity (BLS) entropy is defined in a network consisting of a single node and branches. In this study, we mapped the binary time-series signal to the circumference of the time circle so that the BLS entropy can be calculated for the binary time-series. We obtained the BLS entropy values for “1” signals on the time circle. The set of values are the BLS entropy profile. We selected the local maximum (minimum) point, slope, and inflection point of the entropy profile as the characteristic features of the binary time-series and investigated and explored their significance. The local maximum (minimum) point indicates the time at which the rate of change in the signal density becomes zero. The slope and inflection points correspond to the degree of change in the signal density and the time at which the signal density changes occur, respectively. Moreover, we show that the characteristic features can be widely used in binary time-series analysis by characterizing the movement trajectory of Caenorhabditis elegans. We also mention the problems that need to be explored mathematically in relation to the features and propose candidates for additional features based on the BLS entropy profile. Full article
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Article
An Information Theory Approach to Aesthetic Assessment of Visual Patterns
Entropy 2021, 23(2), 153; https://0-doi-org.brum.beds.ac.uk/10.3390/e23020153 - 27 Jan 2021
Cited by 1 | Viewed by 879
Abstract
The question of beauty has inspired philosophers and scientists for centuries. Today, the study of aesthetics is an active research topic in fields as diverse as computer science, neuroscience, and psychology. Measuring the aesthetic appeal of images is beneficial for many applications. In [...] Read more.
The question of beauty has inspired philosophers and scientists for centuries. Today, the study of aesthetics is an active research topic in fields as diverse as computer science, neuroscience, and psychology. Measuring the aesthetic appeal of images is beneficial for many applications. In this paper, we will study the aesthetic assessment of simple visual patterns. The proposed approach suggests that aesthetically appealing patterns are more likely to deliver a higher amount of information over multiple levels in comparison with less aesthetically appealing patterns when the same amount of energy is used. The proposed approach is evaluated using two datasets; the results show that the proposed approach is more accurate in classifying aesthetically appealing patterns compared to some related approaches that use different complexity measures. Full article
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Article
Entropy Analysis of COVID-19 Cardiovascular Signals
Entropy 2021, 23(1), 87; https://0-doi-org.brum.beds.ac.uk/10.3390/e23010087 - 09 Jan 2021
Cited by 7 | Viewed by 1079
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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Article
A New Hybrid Possibilistic-Probabilistic Decision-Making Scheme for Classification
Entropy 2021, 23(1), 67; https://0-doi-org.brum.beds.ac.uk/10.3390/e23010067 - 03 Jan 2021
Cited by 1 | Viewed by 819
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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Article
A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees
Entropy 2021, 23(1), 20; https://0-doi-org.brum.beds.ac.uk/10.3390/e23010020 - 25 Dec 2020
Cited by 5 | Viewed by 929
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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Article
Modified Gerchberg–Saxton (G-S) Algorithm and Its Application
Entropy 2020, 22(12), 1354; https://0-doi-org.brum.beds.ac.uk/10.3390/e22121354 - 30 Nov 2020
Cited by 6 | Viewed by 916
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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Article
A Chaotic-Based Encryption/Decryption Framework for Secure Multimedia Communications
Entropy 2020, 22(11), 1253; https://0-doi-org.brum.beds.ac.uk/10.3390/e22111253 - 04 Nov 2020
Cited by 10 | Viewed by 1083
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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Article
An Image-Based Class Retrieval System for Roman Republican Coins
Entropy 2020, 22(8), 799; https://0-doi-org.brum.beds.ac.uk/10.3390/e22080799 - 22 Jul 2020
Cited by 1 | Viewed by 986
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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