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J. Imaging
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Advances in Color Imaging, Volume II
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Advances in Color Imaging, Volume II

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Color, Multi-spectral, and Hyperspectral Imaging".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6486

Special Issue Editors

Dr. Vien Cheung

E-Mail Website
Guest Editor
School of Design, University of Leeds, Leeds, Leeds LS2 9JT, UK
Interests: color vision; color science; cross-media reproduction; color management; spectral imaging; color design
Special Issues, Collections and Topics in MDPI journals
Dr. Jean-Baptiste Thomas

E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, 2806 Gjøvik, Norway
Interests: spectral imaging; material appearance; colour imaging; computational appearance
Special Issues, Collections and Topics in MDPI journals
Dr. Peter Rhodes

E-Mail Website
Guest Editor
School of Design, University of Leeds, Leeds LS2 9JT, UK
Interests: image capture and reproduction; color specification, management, and visualization; appearance measurement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Color imaging, spanning the capture, encoding, compression, and reproduction of color images, is an important part of today’s digital world. Decades of color imaging research have provided a solid foundation to enable a wide range of applications: appearance measurement, artwork archiving and restoration, computer gaming, security and camouflage imaging, multispectral or medical diagnosis, etc. Nevertheless, new technologies such as HDR imaging, wide color gamut, 2D and 3D printing, illumination, and immersive realities are rapidly developing to further enhance user experience. How do we make the most of comprehensive color imaging research using these new technologies? The theme of this Special Issue of Journal of Imaging is advances in color imaging. In addition to original research papers with novel findings and review articles describing the current state of the art, papers discussing outstanding issues that need further scientific research and future research directions are invited. Transdisciplinary studies using or developing color imaging are also welcome.

Dr. Vien Cheung
Dr. Jean-Baptiste Thomas
Dr. Peter Rhodes
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. 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 1800 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

  • color vision and perception
  • computational color and color coding
  • color and material appearance modeling
  • color in illumination and lighting
  • 2D and 3D printing
  • image quality and visualization
  • spectral imaging
  • Augmented reality (AR), virtual reality (VR), mixed reality (MR)

Published Papers (4 papers)

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Research

22 pages, 2494 KiB  
Article
Individual Contrast Preferences in Natural Images
by Olga Cherepkova, Seyed Ali Amirshahi and Marius Pedersen
J. Imaging 2024, 10(1), 25; https://doi.org/10.3390/jimaging10010025 - 18 Jan 2024
Viewed by 1242
Abstract
This paper is an investigation in the field of personalized image quality assessment with the focus of studying individual contrast preferences for natural images. To achieve this objective, we conducted an in-lab experiment with 22 observers who assessed 499 natural images and collected [...] Read more.
This paper is an investigation in the field of personalized image quality assessment with the focus of studying individual contrast preferences for natural images. To achieve this objective, we conducted an in-lab experiment with 22 observers who assessed 499 natural images and collected their contrast level preferences. We used a three-alternative forced choice comparison approach coupled with a modified adaptive staircase algorithm to dynamically adjust the contrast for each new triplet. Through cluster analysis, we clustered observers into three groups based on their preferred contrast ranges: low contrast, natural contrast, and high contrast. This finding demonstrates the existence of individual variations in contrast preferences among observers. To facilitate further research in the field of personalized image quality assessment, we have created a database containing 10,978 original contrast level values preferred by observers, which is publicly available online. Full article
(This article belongs to the Special Issue Advances in Color Imaging, Volume II)
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14 pages, 3480 KiB  
Article
Mapping Quantitative Observer Metamerism of Displays
by Giorgio Trumpy, Casper Find Andersen, Ivar Farup and Omar Elezabi
J. Imaging 2023, 9(10), 227; https://doi.org/10.3390/jimaging9100227 - 19 Oct 2023
Viewed by 1541
Abstract
Observer metamerism (OM) is the name given to the variability between the color matches that individual observers consider accurate. The standard color imaging approach, which uses color-matching functions of a single representative observer, does not accurately represent every individual observer’s perceptual properties. This [...] Read more.
Observer metamerism (OM) is the name given to the variability between the color matches that individual observers consider accurate. The standard color imaging approach, which uses color-matching functions of a single representative observer, does not accurately represent every individual observer’s perceptual properties. This paper investigates OM in color displays and proposes a quantitative assessment of the OM distribution across the chromaticity diagram. An OM metric is calculated from a database of individual LMS cone fundamentals and the spectral power distributions of the display’s primaries. Additionally, a visualization method is suggested to map the distribution of OM across the display’s color gamut. Through numerical assessment of OM using two distinct publicly available sets of individual observers’ functions, the influence of the selected dataset on the intensity and distribution of OM has been underscored. The case study of digital cinema has been investigated, specifically the transition from xenon-arc to laser projectors. The resulting heatmaps represent the “topography” of OM for both types of projectors. The paper also presents color difference values, showing that achromatic highlights could be particularly prone to disagreements between observers in laser-based cinema theaters. Overall, this study provides valuable resources for display manufacturers and researchers, offering insights into observer metamerism and facilitating the development of improved display technologies. Full article
(This article belongs to the Special Issue Advances in Color Imaging, Volume II)
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42 pages, 10101 KiB  
Article
Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information
by Srikanth Rangu, Rajagopal Veramalla, Surender Reddy Salkuti and Bikshalu Kalagadda
J. Imaging 2023, 9(4), 74; https://doi.org/10.3390/jimaging9040074 - 23 Mar 2023
Cited by 3 | Viewed by 1397
Abstract
The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is [...] Read more.
The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is a challenging and complex issue, especially for color image segmentation. To moderate this difficulty, a novel multilevel thresholding approach is proposed in this paper based on the electromagnetism optimization (EMO) technique with an energy curve, named multilevel thresholding based on EMO and energy curve (MTEMOE). To compute the optimized threshold values, Otsu’s variance and Kapur’s entropy are deployed as fitness functions; both values should be maximized to locate optimal threshold values. In both Kapur’s and Otsu’s methods, the pixels of an image are classified into different classes based on the threshold level selected on the histogram. Optimal threshold levels give higher efficiency of segmentation; the EMO technique is used to find optimal thresholds in this research. The methods based on an image’s histograms do not possess the spatial contextual information for finding the optimal threshold levels. To abolish this deficiency an energy curve is used instead of the histogram and this curve can establish the spatial relationship of pixels with their neighbor pixels. To study the experimental results of the proposed scheme, several color benchmark images are considered at various threshold levels and compared with other meta-heuristic algorithms: multi-verse optimization, whale optimization algorithm, and so on. The investigational results are illustrated in terms of mean square error, peak signal-to-noise ratio, the mean value of fitness reach, feature similarity, structural similarity, variation of information, and probability rand index. The results reveal that the proposed MTEMOE approach overtops other state-of-the-art algorithms to solve engineering problems in various fields. Full article
(This article belongs to the Special Issue Advances in Color Imaging, Volume II)
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18 pages, 6081 KiB  
Article
Spectral Reflectance Estimation from Camera Responses Using Local Optimal Dataset
by Shoji Tominaga and Hideaki Sakai
J. Imaging 2023, 9(2), 47; https://doi.org/10.3390/jimaging9020047 - 17 Feb 2023
Cited by 2 | Viewed by 1666
Abstract
A novel method is proposed to estimate surface-spectral reflectance from camera responses using a local optimal reflectance dataset. We adopt a multispectral imaging system that involves an RGB camera capturing multiple images under multiple light sources. A spectral reflectance database is utilized to [...] Read more.
A novel method is proposed to estimate surface-spectral reflectance from camera responses using a local optimal reflectance dataset. We adopt a multispectral imaging system that involves an RGB camera capturing multiple images under multiple light sources. A spectral reflectance database is utilized to locally determine the candidates to optimally estimate the spectral reflectance. The proposed estimation method comprises two stages: (1) selecting the local optimal reflectance dataset and (2) determining the best estimate using only the local optimal dataset. In (1), the camera responses are predicted for the respective reflectances in the database, and then the prediction errors are calculated to select the local optimal dataset. In (2), multiple methods are used; in particular, the Wiener and linear minimum mean square error estimators are used to calculate all statistics, based only on the local optimal dataset, and linear and quadratic programming methods are used to solve optimization problems with constraints. Experimental results using different mobile phone cameras show that the estimation accuracy has improved drastically. A much smaller local optimal dataset among spectral reflectance databases is enough to obtain the optimal estimates. The method has potential applications including fields of color science, image science and technology, computer vision, and graphics. Full article
(This article belongs to the Special Issue Advances in Color Imaging, Volume II)
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