Color Models in Image Processing: A Review and Experimental Comparison
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914387286032384 |
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| author | Muratbekova, Muragul Toganas, Nuray Igali, Ayan Shagyrov, Maksat Kadyrgali, Elnara Yerkin, Adilet Shamoi, Pakizar |
| author_facet | Muratbekova, Muragul Toganas, Nuray Igali, Ayan Shagyrov, Maksat Kadyrgali, Elnara Yerkin, Adilet Shamoi, Pakizar |
| contents | Color representation is essential in computer vision and human-computer interaction. There are multiple color models available. The choice of a suitable color model is critical for various applications. This paper presents a review of color models and spaces, analyzing their theoretical foundations, computational properties, and practical applications. We explore traditional models such as RGB, CMYK, and YUV, perceptually uniform spaces like CIELAB and CIELUV, and fuzzy-based approaches as well. Additionally, we conduct a series of experiments to evaluate color models from various perspectives, like device dependency, chromatic consistency, and computational complexity. Our experimental results reveal gaps in existing color models and show that the HS* family is the most aligned with human perception. The review also identifies key strengths and limitations of different models and outlines open challenges and future directions This study provides a reference for researchers in image processing, perceptual computing, digital media, and any other color-related field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00584 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Color Models in Image Processing: A Review and Experimental Comparison Muratbekova, Muragul Toganas, Nuray Igali, Ayan Shagyrov, Maksat Kadyrgali, Elnara Yerkin, Adilet Shamoi, Pakizar Computer Vision and Pattern Recognition Color representation is essential in computer vision and human-computer interaction. There are multiple color models available. The choice of a suitable color model is critical for various applications. This paper presents a review of color models and spaces, analyzing their theoretical foundations, computational properties, and practical applications. We explore traditional models such as RGB, CMYK, and YUV, perceptually uniform spaces like CIELAB and CIELUV, and fuzzy-based approaches as well. Additionally, we conduct a series of experiments to evaluate color models from various perspectives, like device dependency, chromatic consistency, and computational complexity. Our experimental results reveal gaps in existing color models and show that the HS* family is the most aligned with human perception. The review also identifies key strengths and limitations of different models and outlines open challenges and future directions This study provides a reference for researchers in image processing, perceptual computing, digital media, and any other color-related field. |
| title | Color Models in Image Processing: A Review and Experimental Comparison |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.00584 |