article

Visual calibration and correction for ambient illumination

Authors:

Alan Chalmers, and

Erik ReinhardAuthors Info & Claims

ACM Transactions on Applied Perception (TAP), Volume 3, Issue 4

Pages 429 - 452

https://doi.org/10.1145/1190036.1190042

Published: 01 October 2006 Publication History

Abstract

Many applications require that an image will appear the same regardless of where or how it is displayed. However, the conditions in which an image is displayed can adversely affect its appearance. Computer monitor screens not only emit light, but can also reflect extraneous light present in the viewing environment. This can cause images displayed on a monitor to appear faded by reducing their perceived contrast. Current approaches to this problem involve measuring this ambient illumination with specialized hardware and then altering the display device or changing the viewing conditions. This is not only impractical, but also costly and time consuming. For a user who does not have the equipment, expertise, or budget to control these facets, a practical alternative is sought. This paper presents a method whereby the display device itself can be used to determine the effect of ambient light on perceived contrast, thus enabling the viewers themselves to perform visual calibration. This method is grounded in established psychophysical experimentation and we present both an extensive procedure and an equivalent rapid procedure. Our work is extended by providing a novel method of contrast correction so that the contrast of an image viewed in bright conditions can be corrected to appear the same as an image viewed in a darkened room. This is verified through formal validation. These methods are easy to apply in practical settings, while accurate enough to be useful.

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Cited By

Qian YChen SHsiang EAkimoto HLin CWu S(2024)Enhancing a Display’s Sunlight Readability with Tone MappingPhotonics10.3390/photonics1106057811:6(578)Online publication date: 20-Jun-2024
https://doi.org/10.3390/photonics11060578
Büyükarıkan BÜlker E(2023)Convolutional neural network-based apple images classification and image quality measurement by light colors using the color-balancing approachMultimedia Systems10.1007/s00530-023-01084-z29:3(1651-1661)Online publication date: 29-Mar-2023
https://dl.acm.org/doi/10.1007/s00530-023-01084-z
Lee KKim JRyu JKim J(2020)Ambient light robust gamut mapping for optical see-through displaysOptics Express10.1364/OE.39144728:10(15392)Online publication date: 6-May-2020
https://doi.org/10.1364/OE.391447
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Index Terms

Visual calibration and correction for ambient illumination
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
    2. Rendering
2. Human-centered computing
  1. Accessibility
  2. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Transactions on Applied Perception

ACM Transactions on Applied Perception Volume 3, Issue 4

October 2006

120 pages

ISSN:1544-3558

EISSN:1544-3965

DOI:10.1145/1190036

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Copyright © 2006 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2006

Published in TAP Volume 3, Issue 4

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Cited By

Qian YChen SHsiang EAkimoto HLin CWu S(2024)Enhancing a Display’s Sunlight Readability with Tone MappingPhotonics10.3390/photonics1106057811:6(578)Online publication date: 20-Jun-2024
https://doi.org/10.3390/photonics11060578
Büyükarıkan BÜlker E(2023)Convolutional neural network-based apple images classification and image quality measurement by light colors using the color-balancing approachMultimedia Systems10.1007/s00530-023-01084-z29:3(1651-1661)Online publication date: 29-Mar-2023
https://dl.acm.org/doi/10.1007/s00530-023-01084-z
Lee KKim JRyu JKim J(2020)Ambient light robust gamut mapping for optical see-through displaysOptics Express10.1364/OE.39144728:10(15392)Online publication date: 6-May-2020
https://doi.org/10.1364/OE.391447
Ariffin SJamil N(2019)Illumination Classification based on No-Reference Image Quality Assessment (NR-IQA)Proceedings of the 2019 Asia Pacific Information Technology Conference10.1145/3314527.3314529(70-74)Online publication date: 25-Jan-2019
https://dl.acm.org/doi/10.1145/3314527.3314529
Bashford-Rogers TMelo MMarnerides DBessa MDebattista KChalmers A(2019)Learning Preferential Perceptual Exposure for HDR DisplaysIEEE Access10.1109/ACCESS.2019.28989107(36800-36809)Online publication date: 2019
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Kučera JScott JChen NOlivier PHodges S(2017)Towards Calm DisplaysProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/30900811:2(1-21)Online publication date: 30-Jun-2017
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Yang CZhang JChang W(2016)Fast image enhancement method for display images in ambient lightJournal of the Optical Society of America A10.1364/JOSAA.33.00022833:2(228)Online publication date: 29-Jan-2016
https://doi.org/10.1364/JOSAA.33.000228
Mantel CSøgaard JBech SKorhonen JPedersen JForchhammer S(2016)Modeling the Quality of Videos Displayed With Local Dimming Backlight at Different Peak White and Ambient Light LevelsIEEE Transactions on Image Processing10.1109/TIP.2016.257639925:8(3751-3761)Online publication date: 1-Aug-2016
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Yang CChang WLi J(2016)A perceptually optimized mapping technique for display imagesJournal of the Society for Information Display10.1002/jsid.50124:9(576-586)Online publication date: 21-Sep-2016
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