Evidence map›Paper›PMID 41564014›Full record

ArticlePloS one2026

Color adjustment of brand logos for dark mode display.

Byeongjin Kim, Giyun Lee, Hyeon-Jeong Suk

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Byeongjin KimDepartment of Industrial Design, KAIST, Daejeon, South Korea.
Giyun LeeDepartment of Industrial Design, KAIST, Daejeon, South Korea.
Hyeon-Jeong SukDepartment of Industrial Design, KAIST, Daejeon, South Korea.ORCID https://orcid.org/0000-0002-0199-6183

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dark mode has become a standard feature across digital interfaces due to its visual comfort and aesthetic appeal. However, most brand logos are originally designed for light backgrounds, and when directly applied to dark backgrounds, they often suffer from color distortion, reduced visibility, and visual discomfort. These issues can negatively impact both brand identity and user experience. This study aims to propose a systematic adjustment model to optimize brand logo colors in dark mode environments. The research consisted of two experiments. In the first experiment, 31 design-major students manually adjusted 18 fictitious logos with diverse colors on a black background. The analysis revealed systematic trends in color modification, with bright colors shifting toward darker values, dark colors becoming lighter, and chroma showing an overall reduction. Additionally, red and blue hues required hue-angle corrections. Based on these findings, a convergence surface for color adjustment was constructed using Kriging interpolation, leading to the development of a predictive model applicable to new logo colors. The second experiment evaluated the model through a preference survey with 89 participants, using a set of 36 logos, including 18 fictitious logos and 18 commercial logos. Participants compared original logos with those adjusted by the proposed model. The adjusted versions were generally preferred, with the effect being particularly pronounced for logos originally featuring dark colors. The proposed model offers design principles that ensure both brand consistency and visual comfort. By integrating perceptual evidence with empirical validation, this approach provides a stable method for maintaining brand color representation in digital environments and demonstrates applicability to a wider range of graphic elements in dark mode.

Identifiers

PMID41564014
PMCPMC12822999

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.