CQ100: a high-quality image dataset for color quantization research
[EN]Color quantization ( CQ ) is a classical image processing operation that reduces the number of distinct colors in a given image. Although the idea of CQ dates back to the early 1970s, the first true CQ algorithm, median-cut, was proposed later in 1980. Since then, hundreds of publications have i...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/161086 |
| Acceso en línea: | http://hdl.handle.net/10366/161086 |
| Access Level: | acceso abierto |
| Palabra clave: | Image processing Data clustering Dataset CQ100 Color quantization |
| Sumario: | [EN]Color quantization ( CQ ) is a classical image processing operation that reduces the number of distinct colors in a given image. Although the idea of CQ dates back to the early 1970s, the first true CQ algorithm, median-cut, was proposed later in 1980. Since then, hundreds of publications have investigated the topic of CQ, proposing dozens of algorithms. A vast majority of these publications demonstrate their results on small datasets, containing a handful of images of mixed quality. Furthermore, the reproducibility of CQ research is often limited due to the use of private test images or public test images with multiple non-identical copies on the World Wide Web or restrictive licenses. To address these problems, we curated a large, diverse, and high-quality dataset of 24-bit color images called CQ 100 and released it under a permissive license. We present an overview of CQ 100 and demonstrate its use in comparing CQ algorithms. |
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