Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Advances in Perceptual Image Quality Metrics
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Perceptual Image Quality Metrics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 8408

Special Issue Editors

Prof. Dr. Marius Pedersen

E-Mail Website
Guest Editor
Norwegian University of Science and Technology, 7491 Trondheim, Norway
Interests: creating perceptual image quality metrics; model the human visual system
Prof. Dr. Aladine Chetouani

E-Mail Website
Guest Editor
Laboratory PRISME, University of Orleans, Château de la Source, 45100 Orléans, France
Interests: pattern recognition; image quality assessment; video analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances are rapidly taking place in the imaging industry, with new products introduced to the market. To evaluate and benchmark image quality, objective omage quality metrics have become very popular. This Special Issue aims to present new research on perceptual image quality metrics including but not limited to full-reference metrics, no-reference metrics, reduced-reference metrics, new databases for the evaluation of image quality metrics, pooling techniques, and perceptual models for use in image quality metrics.

Prof. Dr. Marius Pedersen
Prof. Dr. Aladine Chetouani
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. Applied Sciences 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 metrics
  • perception
  • quality assessment
  • quality databases

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 42054 KiB  
Article
Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
by Altynay Kadyrova, Vlado Kitanovski and Marius Pedersen
Appl. Sci. 2021, 11(16), 7470; https://doi.org/10.3390/app11167470 - 14 Aug 2021
Cited by 1 | Viewed by 2131
Abstract
Quality assessment is an important aspect in a variety of application areas. In this work, the objective quality assessment of 2.5D prints was performed. The work is done on camera captures under both diffuse (single-shot) and directional (multiple-shot) illumination. Current state-of-the-art 2D full-reference [...] Read more.
Quality assessment is an important aspect in a variety of application areas. In this work, the objective quality assessment of 2.5D prints was performed. The work is done on camera captures under both diffuse (single-shot) and directional (multiple-shot) illumination. Current state-of-the-art 2D full-reference image quality metrics were used to predict the quality of 2.5D prints. The results showed that the selected metrics can detect differences between the prints as well as between a print and its 2D reference image. Moreover, the metrics better detected differences in the multiple-shot set-up captures than in the single-shot set-up ones. Although the results are based on a limited number of images, they show existing metrics’ ability to work with 2.5D prints under limited conditions. Full article
(This article belongs to the Special Issue Advances in Perceptual Image Quality Metrics)
►▼ Show Figures

Figure 1

21 pages, 6997 KiB  
Article
Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance
by Aladine Chetouani and Marius Pedersen
Appl. Sci. 2021, 11(10), 4661; https://doi.org/10.3390/app11104661 - 19 May 2021
Cited by 9 | Viewed by 2843
Abstract
An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality [...] Read more.
An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability. Full article
(This article belongs to the Special Issue Advances in Perceptual Image Quality Metrics)
►▼ Show Figures

Figure 1

17 pages, 15764 KiB  
Article
Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
by Hafiz Muhammad Usama Hassan Alvi, Muhammad Shahid Farid, Muhammad Hassan Khan and Marcin Grzegorzek
Appl. Sci. 2021, 11(6), 2666; https://doi.org/10.3390/app11062666 - 17 Mar 2021
Cited by 2 | Viewed by 2213
Abstract
Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience [...] Read more.
Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available views and their depth maps using depth-image-based rendering (DIBR) techniques. The quality of a DIBR-synthesized image may be compromised for several reasons, e.g., inaccurate depth estimation. Since depth is important in this application, inaccuracies in depth maps lead to different textural and structural distortions that degrade the quality of the generated image and result in a poor quality of experience (QoE). Therefore, quality assessment DIBR-generated images are essential to guarantee an appreciative QoE. This paper aims at estimating the quality of DIBR-synthesized images and proposes a novel 3D objective image quality metric. The proposed algorithm aims to measure both textural and structural distortions in the DIBR image by exploiting the contrast sensitivity and the Hausdorff distance, respectively. The two measures are combined to estimate an overall quality score. The experimental evaluations performed on the benchmark MCL-3D dataset show that the proposed metric is reliable and accurate, and performs better than existing 2D and 3D quality assessment metrics. Full article
(This article belongs to the Special Issue Advances in Perceptual Image Quality Metrics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop