Forty years of color quantization: a modern, algorithmic survey

Celebi, M. Emre

doi:10.1007/s10462-023-10406-6

Cited by 6 publications

(8 citation statements)

References 350 publications

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“…Quantitative assessment remains to be one of the least explored aspects of CQ. 8 Most CQ studies employ pixelwise image fidelity metrics such as the mean squared error (MSE), or its variants such as the peak signal-to-noise ratio (PSNR), computed in the RGB color space. Recently, Ref.…”

Section: Quantitative Assessment and Discussionmentioning

confidence: 99%

“…• Sources: Wikimedia Commons (73), PxHere (23), and Kodak lossless true color image suite (4). • Categories: animals (18), food (17), miscellaneous (6), objects (23), people (8), places (11), plants (6), and vehicles (11). • Licenses: public domain (14), CC0 (25), CC BY (7), and CC BY-SA (54).…”

Section: Description Of the Datasetmentioning

confidence: 99%

“…7 Early CQ algorithms were mostly hierarchical, whereas modern CQ algorithms tend to be partitional. 8 Yet another classification scheme is based on whether or not the palette size is allowed to change during the CQ process. Static algorithms assume that the palette size is a constant to be specified by the user in advance, whereas dynamic algorithms compute the palette size automatically at run-time.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the over four decades of research on CQ, there is currently no benchmark dataset on which CQ algorithms can be developed, tested, and compared. 8 Many CQ studies employ a subset of the USC-SIPI image database (Available at Ref. 14) or the Kodak lossless true color image suite (Available at Ref.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, partitional algorithms find all the clusters simultaneously as a partition of the data without imposing a hierarchical structure on the data 7 . Early cq algorithms were mostly hierarchical, whereas modern cq algorithms tend to be partitional 8 . Yet another classification scheme is based on whether or not the palette size is allowed to change during the cq process.…”

Section: Introductionmentioning

confidence: 99%

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cq100: a high-quality image dataset for color quantization research

Celebi

Delgado

2023

J. Electron. Imag.

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.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 cq100 and released it under a permissive license. We present an overview of cq100 and demonstrate its use in comparing cq algorithms.

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Section: Quantitative Assessment and Discussionmentioning

confidence: 99%

Section: Description Of the Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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cq100: a high-quality image dataset for color quantization research

Celebi

Delgado

2023

J. Electron. Imag.

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A comparative study of color quantization methods using various image quality assessment indices

Pérez-Delgado,

Celebi

2024

Multimedia Systems

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This article analyzes various color quantization methods using multiple image quality assessment indices. Experiments were conducted with ten color quantization methods and eight image quality indices on a dataset containing 100 RGB color images. The set of color quantization methods selected for this study includes well-known methods used by many researchers as a baseline against which to compare new methods. On the other hand, the image quality assessment indices selected are the following: mean squared error, mean absolute error, peak signal-to-noise ratio, structural similarity index, multi-scale structural similarity index, visual information fidelity index, universal image quality index, and spectral angle mapper index. The selected indices not only include the most popular indices in the color quantization literature but also more recent ones that have not yet been adopted in the aforementioned literature. The analysis of the results indicates that the conventional assessment indices used in the color quantization literature generate different results from those obtained by newer indices that take into account the visual characteristics of the images. Therefore, when comparing color quantization methods, it is recommended not to use a single index based solely on pixelwise comparisons, as is the case with most studies to date, but rather to use several indices that consider the various characteristics of the human visual system.

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Color quantization using an accelerated Jancey k-means clustering algorithm

Bounds,

Celebi,

Maxwell

2024

J. Electron. Imag.

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Color quantization (CQ) is a fixed-rate vector quantization developed for color images to reduce their number of distinct colors while keeping the resulting distortion to a minimum. Various clustering algorithms have been adapted to the CQ problem over the past 40 years. Among these, hierarchical algorithms are generally more efficient (i.e., faster), whereas partitional ones are more effective (in minimizing distortion). Among the partitional algorithms, the effectiveness and efficiency of the Lloyd (or batch) k -means algorithm have been shown by multiple recent studies. We investigate an alternative, lesser-known k -means algorithm proposed by Jancey, which differs from Lloyd k -means (LKM) in the way it updates the cluster centers at the end of each iteration. To obtain a competitive color quantizer, we develop a weighted variant of Jancey k -means (JKM) and then accelerate the weighted algorithm using the triangle inequality. Through extensive experiments on 100 color images, we demonstrate that, with the proposed modifications, JKM outperforms LKM significantly in terms of efficiency without sacrificing effectiveness. In addition, the proposed JKM-based color quantizer is as straightforward to implement as the popular LKM color quantizer.

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Forty years of color quantization: a modern, algorithmic survey

Cited by 6 publications

References 350 publications

cq100: a high-quality image dataset for color quantization research

cq100: a high-quality image dataset for color quantization research

A comparative study of color quantization methods using various image quality assessment indices

Color quantization using an accelerated Jancey k-means clustering algorithm

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