Special Issue "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: 31 August 2021.

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 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 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. 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 2000 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

Published Papers (2 papers)

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Research

Article
Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance
Appl. Sci. 2021, 11(10), 4661; https://doi.org/10.3390/app11104661 - 19 May 2021
Viewed by 313
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)
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Article
Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
Appl. Sci. 2021, 11(6), 2666; https://doi.org/10.3390/app11062666 - 17 Mar 2021
Viewed by 387
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)
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