Color constancy in Japanese animation

Ichihara, Yasuyo G.

doi:10.1117/12.639253

Cited by 2 publications

(1 citation statement)

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“…-0.3920 -0.3920 *T hese streams have animation content and hence technically, should not be flagged as they are subject to the animator's artistic intent. However, animation detection [26], [27] is a separate component of the overall flow and will be dealt with in the future.…”

Section: Resultsmentioning

confidence: 99%

Machine learning based chroma phase offset detection and correction in motion video

Mogre¹,

Madnani²

2022

IJ-AI

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Generally, chroma phase or hue offset issues within a scene are hard to detect, without a reference or context (i.e. some apriori knowledge about how certain objects within the scene should actually appear in terms of their hue). Moreover, when it comes to skin/flesh tones, hue deviation can be noticeable and can markedly degrade the viewer quality of experience(QoE), whenever it does occur. However a lot of research has gone into flesh tone detection, specifically, the color gamut within which flesh tone is present. This topic has been well documented in the literature with respect to various color spaces: red, green, blue (RGB) and YIQ. Therefore, overall issues with chroma offset or hue within the video content could potentially be approached by extracting and analyzing a reliable reference, such as skin or flesh tone (if present), within some allowable deviation. This involves machine learning (ML) based facial recognition and tracking followed by skin tone region recognition within the detected facial sequence (i.e. Region of Interest). The skin region serves as a ‘self-reference’ in order to discern any inherent phase offset within the content. Finally, the angular chroma deviation discerned can then be used for subsequent correction as well.

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Section: Resultsmentioning

confidence: 99%

Machine learning based chroma phase offset detection and correction in motion video

Mogre¹,

Madnani²

2022

IJ-AI

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Color universal design: the selection of four easily distinguishable colors for all color vision types

Ichihara

Okabe

Iga³

et al. 2008

SPIE Proceedings

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The objective of this project is to establish a practical application of the concept of Color Universal Design (CUD), the design that is recognizable to all color vision types. In our research, we looked for a clearly distinguishable combination of hues of four colors -black, red, green, and bluewhich are frequently used in these circumstances. Red-green confusion people do not confuse all kinds of red and all kinds of green. By selecting particular hues for each color, the ability to distinguish between the four colors should be greatly improved. Our study thus concluded that, by carefully selecting hues within the range of each color category, it is possible to establish color-combinations which are easily distinguishable to people of all color-vision types in order to facilitate visual communication.

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Color constancy in Japanese animation

Cited by 2 publications

References 0 publications

Machine learning based chroma phase offset detection and correction in motion video

Machine learning based chroma phase offset detection and correction in motion video

Color universal design: the selection of four easily distinguishable colors for all color vision types

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