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Towards Image/Video Perception with Entropy-Aware Features and Its Applications

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 17535

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Special Issue Editors

Prof. Dr. Ke Gu

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Guest Editor

Faculty of Information Technology, Beijing University of Technology, Beijing, China
Interests: environmental perception; image processing; quality assessment; machine learning

Dr. Weiling Chen

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Assistant Guest Editor

Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
Interests: multimedia processing; quality perception; underwater acoustic communication

Dr. Vinit Jakhetiya

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Assistant Guest Editor

Indian Institute of Technology, Jammu, India
Interests: image/video processing; quality assessment; visual perceptual modeling

Dr. Jiheng Wang

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Assistant Guest Editor

Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: biomedical image and signal processing; image and video quality assessment; perceptual video coding; deep learning

Special Issue Information

Dear Colleagues,

Entropy is a good measurement of visual perceptual information and visual uncertainty, since it is capable of evaluating the amount of information that the multimedia source conveys to the human eyes. It is obvious that the perceptual information for the human visual system is crucial in many vision-related applications. In quality monitoring, the quality degradation varies as the perceptual information changes. As for visual enhancements, perceptual information is regarded as a good indicator of contrast. Moreover, visual uncertainty minimization can also be utilized in base selection, data segmentation, and reconstruction for images. However, limited efforts have been devoted to systematically and synthetically analyzing visual perceptual information. Its influence on visual perception and processing have not yet been fully investigated.

Today, with the deeper understanding of visual perception and increasing demands for higher effectiveness in visual signal processing, new opportunities are emerging in visual perception, modeling, imaging and processing based on the evaluation of visual information. This Special Issue aims at promoting cutting-edge research along this direction and offering a timely collection of work for researchers. We welcome high-quality, original submissions related to entropy-aware image/video perception.

The topics of interest include but are not limited to:

Entropy-related feature modeling;
Objective image/video quality perception based on entropy;
Entropy-aware image/video understanding;
Entropy-aware imaging;
Virtual reality video processing;
Augmented reality video processing;
The analysis of multimedia systems;
The analysis and measurement of the amount of information for multimedia;
Machine learning;
Naturalness statistic modeling;
Vision modeling based on entropy;
Objective detection based on the analysis of entropy;
Image/video coding considering entropy;
Other entropy-based multimedia signal processing.

Prof. Dr. Ke Gu
Dr. Weiling Chen
Dr. Vinit Jakhetiya
Dr. Jiheng Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

entropy-aware systems
quality perception
image/video processing
entropy-related features
multimedia system
visual perception

Published Papers (6 papers)

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Research

12 pages, 3027 KiB

Open AccessArticle

Bird Species Identification Using Spectrogram Based on Multi-Channel Fusion of DCNNs

by Feiyu Zhang, Luyang Zhang, Hongxiang Chen and Jiangjian Xie

Entropy 2021, 23(11), 1507; https://doi.org/10.3390/e23111507 - 13 Nov 2021

Cited by 12 | Viewed by 2399

Abstract

Deep convolutional neural networks (DCNNs) have achieved breakthrough performance on bird species identification using a spectrogram of bird vocalization. Aiming at the imbalance of the bird vocalization dataset, a single feature identification model (SFIM) with residual blocks and modified, weighted, cross-entropy function was proposed. To further improve the identification accuracy, two multi-channel fusion methods were built with three SFIMs. One of these fused the outputs of the feature extraction parts of three SFIMs (feature fusion mode), the other fused the outputs of the classifiers of three SFIMs (result fusion mode). The SFIMs were trained with three different kinds of spectrograms, which were calculated through short-time Fourier transform, mel-frequency cepstrum transform and chirplet transform, respectively. To overcome the shortage of the huge number of trainable model parameters, transfer learning was used in the multi-channel models. Using our own vocalization dataset as a sample set, it is found that the result fusion mode model outperforms the other proposed models, the best mean average precision (MAP) reaches 0.914. Choosing three durations of spectrograms, 100 ms, 300 ms and 500 ms for comparison, the results reveal that the 300 ms duration is the best for our own dataset. The duration is suggested to be determined based on the duration distribution of bird syllables. As for the performance with the training dataset of BirdCLEF2019, the highest classification mean average precision (cmAP) reached 0.135, which means the proposed model has certain generalization ability. Full article

(This article belongs to the Special Issue Towards Image/Video Perception with Entropy-Aware Features and Its Applications)

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11 pages, 1189 KiB

Open AccessArticle

Joint Early Stopping Criterions for Protograph LDPC Codes-Based JSCC System in Images Transmission

by Zhiping Xu, Lin Wang and Shaohua Hong

Entropy 2021, 23(11), 1392; https://doi.org/10.3390/e23111392 - 24 Oct 2021

Cited by 4 | Viewed by 1594

Abstract

In this paper, a joint early stopping criterion based on cross entropy (CE), named joint CE criterion, is presented for double-protograph low-density parity-check (DP-LDPC) codes-based joint source-channel coding (JSCC) systems in images transmission to reduce the decoding complexity and decrease the decoding delay. The proposed early stopping criterion adopts the CE from the output likelihood ratios (LLRs) of the joint decoder. Moreover, a special phenomenon named asymmetry oscillation-like convergence (AOLC) in the changing process of CE is uncovered in the source decoder and channel decoder of this system meanwhile, and the proposed joint CE criterion can reduce the impact from the AOLC phenomenon. Comparing to the counterparts, the results show that the joint CE criterion can perform well in the decoding complexity and decoding latency in the low–moderate signal-to-noise ratio (SNR) region and achieve performance improvement in the high SNR region with appropriate parameters, which also demonstrates that this system with joint CE is a low-latency and low-power system. Full article

(This article belongs to the Special Issue Towards Image/Video Perception with Entropy-Aware Features and Its Applications)

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14 pages, 4125 KiB

Open AccessArticle

Improved YOLO Based Detection Algorithm for Floating Debris in Waterway

by Feng Lin, Tian Hou, Qiannan Jin and Aiju You

Entropy 2021, 23(9), 1111; https://doi.org/10.3390/e23091111 - 27 Aug 2021

Cited by 36 | Viewed by 6182

Abstract

Various floating debris in the waterway can be used as one kind of visual index to measure the water quality. The traditional image processing method is difficult to meet the requirements of real-time monitoring of floating debris in the waterway due to the complexity of the environment, such as reflection of sunlight, obstacles of water plants, a large difference between the near and far target scale, and so on. To address these issues, an improved YOLOv5s (FMA-YOLOv5s) algorithm by adding a feature map attention (FMA) layer at the end of the backbone is proposed. The mosaic data augmentation is applied to enhance the detection effect of small targets in training. A data expansion method is introduced to expand the training dataset from 1920 to 4800, which fuses the labeled target objects extracted from the original training dataset and the background images of the clean river surface in the actual scene. The comparisons of accuracy and rapidity of six models of this algorithm are completed. The experiment proves that it meets the standards of real-time object detection. Full article

(This article belongs to the Special Issue Towards Image/Video Perception with Entropy-Aware Features and Its Applications)

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18 pages, 1122 KiB

Open AccessArticle

Improving Perceived Quality of Live Adaptative Video Streaming

by Carlos Eduardo Maffini Santos, Carlos Alexandre Gouvea da Silva and Carlos Marcelo Pedroso

Entropy 2021, 23(8), 948; https://doi.org/10.3390/e23080948 - 25 Jul 2021

Cited by 8 | Viewed by 1887

Abstract

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks. Full article

(This article belongs to the Special Issue Towards Image/Video Perception with Entropy-Aware Features and Its Applications)

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16 pages, 1660 KiB

Open AccessArticle

Subjective and Objective Quality Assessments of Display Products

by Huiqing Zhang, Donghao Li, Yibing Yu and Nan Guo

Entropy 2021, 23(7), 814; https://doi.org/10.3390/e23070814 - 26 Jun 2021

Cited by 4 | Viewed by 1849

Abstract

In recent years, people’s daily lives have become inseparable from a variety of electronic devices, especially mobile phones, which have undoubtedly become necessity in people’s daily lives. In this paper, we are looking for a reliable way to acquire visual quality of the display product so that we can improve the user’s experience with the display product. This paper proposes two major contributions: the first one is the establishment of a new subjective assessment database (DPQAD) of display products’ screen images. Specifically, we invited 57 inexperienced observers to rate 150 screen images showing the display product. At the same time, in order to improve the reliability of screen display quality score, we combined the single stimulation method with the stimulation comparison method to evaluate the newly created display products’ screen images database effectively. The second one is the development of a new no-reference image quality assessment (IQA) metric. For a given image of the display product, first our method extracts 27 features by analyzing the contrast, sharpness, brightness, etc., and then uses the regression module to obtain the visual quality score. Comprehensive experiments show that our method can evaluate natural scene images and screen content images at the same time. Moreover, compared with ten state-of-the-art IQA methods, our method shows obvious superiority on DPQAD. Full article

(This article belongs to the Special Issue Towards Image/Video Perception with Entropy-Aware Features and Its Applications)

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20 pages, 7952 KiB

Open AccessArticle

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

by Chongchong Jin, Zongju Peng, Wenhui Zou, Fen Chen, Gangyi Jiang and Mei Yu

Entropy 2021, 23(6), 770; https://doi.org/10.3390/e23060770 - 18 Jun 2021

Cited by 3 | Viewed by 1971

Abstract

Multiview video plus depth is one of the mainstream representations of 3D scenes in emerging free viewpoint video, which generates virtual 3D synthesized images through a depth-image-based-rendering (DIBR) technique. However, the inaccuracy of depth maps and imperfect DIBR techniques result in different geometric distortions that seriously deteriorate the users’ visual perception. An effective 3D synthesized image quality assessment (IQA) metric can simulate human visual perception and determine the application feasibility of the synthesized content. In this paper, a no-reference IQA metric based on visual-entropy-guided multi-layer features analysis for 3D synthesized images is proposed. According to the energy entropy, the geometric distortions are divided into two visual attention layers, namely, bottom-up layer and top-down layer. The feature of salient distortion is measured by regional proportion plus transition threshold on a bottom-up layer. In parallel, the key distribution regions of insignificant geometric distortion are extracted by a relative total variation model, and the features of these distortions are measured by the interaction of decentralized attention and concentrated attention on top-down layers. By integrating the features of both bottom-up and top-down layers, a more visually perceptive quality evaluation model is built. Experimental results show that the proposed method is superior to the state-of-the-art in assessing the quality of 3D synthesized images. Full article

(This article belongs to the Special Issue Towards Image/Video Perception with Entropy-Aware Features and Its Applications)

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