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Electronics
Special Issues
Advanced Technologies for Image/Video Quality Assessment
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Advanced Technologies for Image/Video Quality Assessment

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 3184

Special Issue Editors

Dr. Shaode Yu

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
Interests: feature engineering; machine learning; computer vision
Dr. Dingquan Li

E-Mail Website
Guest Editor
Network Intelligence Research Department, Peng Cheng Laboratory, Shenzhen 518055, China
Interests: image, video, and point cloud processing

Special Issue Information

Dear Colleagues,

Advanced technologies, such as deep neural networks and reinforcement learning strategies, have been designed and deployed in scientific and industrial fields. These technologies have boosted the performance of multi-media services and enhanced the quality of user experience.

This Special Issue will provide a forum to publish original research papers and review articles covering novel methodologies, applications, and theories of advanced technologies in image and video quality assessment, as well as related topics.

This Special Issue is primarily focused on, but not limited to, the following topics:

  1. Image, medical image and video quality assessment;
  2. Image, medical image and video quality enhancement;
  3. Aesthetic evaluation;
  4. Distortion detection and recovery;
  5. Measuring quality of user experience;
  6. Quality-guided optimization of image processing.

Dr. Shaode Yu
Dr. Dingquan Li
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • image quality assessment
  • video quality assessment
  • human visual system
  • quality of experience
  • quality enhancement
  • distortion detect and recovery
  • machine learning
  • ensemble learning
  • deep learning

Published Papers (2 papers)

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Research

32 pages, 24400 KiB  
Article
Objective Video Quality Assessment and Ground Truth Coordinates for Automatic License Plate Recognition
by Mikołaj Leszczuk, Lucjan Janowski, Jakub Nawała, Jingwen Zhu, Yuding Wang and Atanas Boev
Electronics 2023, 12(23), 4721; https://doi.org/10.3390/electronics12234721 - 21 Nov 2023
Viewed by 1322
Abstract
In the realm of modern video processing systems, traditional metrics such as the Peak Signal-to-Noise Ratio and Structural Similarity are often insufficient for evaluating videos intended for recognition tasks, like object or license plate recognition. Recognizing the need for specialized assessment in this [...] Read more.
In the realm of modern video processing systems, traditional metrics such as the Peak Signal-to-Noise Ratio and Structural Similarity are often insufficient for evaluating videos intended for recognition tasks, like object or license plate recognition. Recognizing the need for specialized assessment in this domain, this study introduces a novel approach tailored to Automatic License Plate Recognition (ALPR). We developed a robust evaluation framework using a dataset with ground truth coordinates for ALPR. This dataset includes video frames captured under various conditions, including occlusions, to facilitate comprehensive model training, testing, and validation. Our methodology simulates quality degradation using a digital camera image acquisition model, representing how luminous flux is transformed into digital images. The model’s performance was evaluated using Video Quality Indicators within an OpenALPR library context. Our findings show that the model achieves a high F-measure score of 0.777, reflecting its effectiveness in assessing video quality for recognition tasks. The proposed model presents a promising avenue for accurate video quality assessment in ALPR tasks, outperforming traditional metrics in typical recognition application scenarios. This underscores the potential of the methodology for broader adoption in video quality analysis for recognition purposes. Full article
(This article belongs to the Special Issue Advanced Technologies for Image/Video Quality Assessment)
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19 pages, 18389 KiB  
Article
Summarization of Videos with the Signature Transform
by J. de Curtò, I. de Zarzà, Gemma Roig and Carlos T. Calafate
Electronics 2023, 12(7), 1735; https://doi.org/10.3390/electronics12071735 - 05 Apr 2023
Cited by 8 | Viewed by 1381
Abstract
This manuscript presents a new benchmark for assessing the quality of visual summaries without the need for human annotators. It is based on the Signature Transform, specifically focusing on the RMSE and the MAE Signature and Log-Signature metrics, and builds upon the assumption [...] Read more.
This manuscript presents a new benchmark for assessing the quality of visual summaries without the need for human annotators. It is based on the Signature Transform, specifically focusing on the RMSE and the MAE Signature and Log-Signature metrics, and builds upon the assumption that uniform random sampling can offer accurate summarization capabilities. We provide a new dataset comprising videos from Youtube and their corresponding automatic audio transcriptions. Firstly, we introduce a preliminary baseline for automatic video summarization, which has at its core a Vision Transformer, an image–text model pre-trained with Contrastive Language–Image Pre-training (CLIP), as well as a module of object detection. Following that, we propose an accurate technique grounded in the harmonic components captured by the Signature Transform, which delivers compelling accuracy. The analytical measures are extensively evaluated, and we conclude that they strongly correlate with the notion of a good summary. Full article
(This article belongs to the Special Issue Advanced Technologies for Image/Video Quality Assessment)
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