Evidence map›Paper›PMID 26974939›Full record

ArticleJournal of the Optical Society of America. A, Optics, image science, and vision2016

A simple principled approach for modeling and understanding uniform color metrics.

Kevin A G Smet, Michael A Webster, Lorne A Whitehead

Abstract read
In one paragraph

Article in Journal of the Optical Society of America. A, Optics, image science, and vision, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Gaining the system: limits to compensating color deficiencies through post-receptoral gain changes.Journal of the Optical Society of America. A, Optics, image science, and vision · 2023
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. The Verriest Lecture: Adventures in blue and yellow.Journal of the Optical Society of America. A, Optics, image science, and vision · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kevin A G Smet
Michael A Webster
Lorne A Whitehead

Funding

SALIENCE, ADAPTATION, AND NATURAL IMAGE STATISTICSR01EY010834 · NEI · UNIVERSITY OF NEVADA RENO · PI WEBSTER, MICHAEL A · 2000 to 2023
$4.6M
COLOR CONTRAST ADAPTATIONR29EY010834 · NEI · UNIVERSITY OF NEVADA RENO · PI WEBSTER, MICHAEL A · 1995 to 1999
–
NEI NIH HHS EY-10834NEI NIH HHS R01 EY010834NEI NIH HHS R29 EY010834
6 · The paper itself

Abstract

An important goal in characterizing human color vision is to order color percepts in a way that captures their similarities and differences. This has resulted in the continuing evolution of "uniform color spaces," in which the distances within the space represent the perceptual differences between the stimuli. While these metrics are now very successful in predicting how color percepts are scaled, they do so in largely empirical, ad hoc ways, with limited reference to actual mechanisms of color vision. In this article our aim is to instead begin with general and plausible assumptions about color coding, and then develop a model of color appearance that explicitly incorporates them. We show that many of the features of empirically defined color order systems (those of Munsell, Pantone, NCS, and others) as well as many of the basic phenomena of color perception, emerge naturally from fairly simple principles of color information encoding in the visual system and how it can be optimized for the spectral characteristics of the environment.

Indexed as

Models, BiologicalAdaptation, OcularColor PerceptionColor VisionHumansLightRetinal Cone Photoreceptor Cells

Identifiers

PMID26974939
PMCPMC4793282

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.