Special Issue "Image and Video Quality Assessment"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Image and Video Processing".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Dr. Seyed Ali Amirshahi
E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
Interests: image quality assessment; video quality assessment; computational aesthetics; perception
Special Issues and Collections in MDPI journals
Dr. Mekides Assefa Abebe
E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
Interests: perceptual imaging; highly dynamic range imaging; computer vision; image and video quality

Special Issue Information

Dear Colleagues,

With the advancement of technology. the need to automatically evaluate the quality of images and videos has become an important part of most image processing and computer vision applications. While current objective image and video quality metrics have shown high correlation with subjective scores, nevertheless, there exist huge room for improvement. This includes but is not limited to difference in the performance of metrics across various datasets and distortions, dealing with multiple distortions, run-time performance, memory requirements, etc. 

In this Special Issue, we aim to address these issues. We encourage contributions presenting methods, techniques, tools, and ideas on how the state-of-the-art could be advanced. We seek original contributions in image and video quality assessment, but not limited to the following:

  • Large scale datasets for image and video quality assessment;
  • Novel methods for subjective evaluations (in particular crowdsourcing);
  • Objective image and video quality assessment;
  • Image and video quality enhancement;
  • Human perception;
  • Aesthetic quality assessment of image and videos;
  • Image and video quality assessment for different environments, including but not limited to printing, virtual reality, high dynamic range, displays, video conferencing, etc.;
  • Medical image and video quality assessment.

Dr. Seyed Ali Amirshahi
Dr. Mekides Assefa Abebe
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 papers will be 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. Journal of Imaging 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 1600 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
  • video quality
  • quality assessment
  • human visual system
  • quality enhancement
  • subjective datasets

Published Papers (1 paper)

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Research

Article
No-Reference Image Quality Assessment with Global Statistical Features
J. Imaging 2021, 7(2), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020029 - 05 Feb 2021
Viewed by 853
Abstract
The perceptual quality of digital images is often deteriorated during storage, compression, and transmission. The most reliable way of assessing image quality is to ask people to provide their opinions on a number of test images. However, this is an expensive and time-consuming [...] Read more.
The perceptual quality of digital images is often deteriorated during storage, compression, and transmission. The most reliable way of assessing image quality is to ask people to provide their opinions on a number of test images. However, this is an expensive and time-consuming process which cannot be applied in real-time systems. In this study, a novel no-reference image quality assessment method is proposed. The introduced method uses a set of novel quality-aware features which globally characterizes the statistics of a given test image, such as extended local fractal dimension distribution feature, extended first digit distribution features using different domains, Bilaplacian features, image moments, and a wide variety of perceptual features. Experimental results are demonstrated on five publicly available benchmark image quality assessment databases: CSIQ, MDID, KADID-10k, LIVE In the Wild, and KonIQ-10k. Full article
(This article belongs to the Special Issue Image and Video Quality Assessment)
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