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Color quantization by dynamic programming and principal analysis

Author:

Xiaolin WuAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 11, Issue 4

Pages 348 - 372

https://doi.org/10.1145/146443.146475

Published: 01 October 1992 Publication History

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Abstract

Color quantization is a process of choosing a set of K representative colors to approximate the N colors of an image, K < N, such that the resulting K-color image looks as much like the original N-color image as possible. This is an optimization problem known to be NP-complete in K. However, this paper shows that by ordering the N colors along their principal axis and partitioning the color space with respect to this ordering, the resulting constrained optimization problem can be solved in O(N + KM²) time by dynamic programming (where M is the intensity resolution of the device).

Traditional color quantization algorithms recursively bipartition the color space. By using the above dynamic-programming algorithm, we can construct a globally optimal K-partition, K>2, of a color space in the principal direction of the input data. This new partitioning strategy leads to smaller quantization error and hence better image quality. Other algorithmic issues in color quantization such as efficient statistical computations and nearest-neighbor searching are also studied. The interplay between luminance and chromaticity in color quantization with and without color dithering is investigated. Our color quantization method allows the user to choose a balance between the image smoothness and hue accuracy for a given K.

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Cited By

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Park JKim SLee JKo J(2023)Scalable Color Quantization for Task-centric Image CompressionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355138919:2s(1-18)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3551389
Pasumurthi VAyyappa Ramineni GKumar Jatoth R(2023)Enhanced Image Quantization Through Hybrid Sine Cosine Optimization and K-Means Clustering2023 3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET)10.1109/ICEFEET59656.2023.10452222(01-05)Online publication date: 21-Dec-2023
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Su SGu LYang YZhang ZHarada T(2023)Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01104(11987-11997)Online publication date: 1-Oct-2023
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Index Terms

Color quantization by dynamic programming and principal analysis
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Document scanning
      2. Graphics recognition and interpretation
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
  2. Computer graphics
    1. Image compression
    2. Image manipulation

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Reviews

Reviewer: Stanley Martin Dunn

An efficient algorithm for color quantization based on principal component analysis and dynamic programming is described. The purpose of this paper was to summarize work done in the field and present the algorithm in the context of what has recently been done. This paper includes previous work, a description of the new algorithm, complexity analysis, and some examples of its use. The length of the paper is suitable and it appears to fulfill its intended purpose. The best features of the paper are the problem formulation and literature survey. I found the mathematics to be presented poorly. Overall, this paper is somewhat difficult to read and contains too much detail for a large part of the intended audience. Since the intention of the paper was to present the algorithm, the analysis included was appropriate, but the author would do well to present the material again with less detail for the larger audience. To read the current version requires an understanding of algorithm analysis, linear algebra, and graphics and image processing. T he main idea can be conveyed with less detail. This paper is harder to read than the two papers that it was compared to, describing the median cut [1] and minimum variance [2] methods. The references provided are adequate to learn about this area of research. Unfortunately, this paper relies too heavily on the reader's ability to abstract concepts that could be easily conveyed with a few simple diagrams. The experiments presented are sparse, heavily concentrating on one of the two images given as raw data. I was left with more questions than answers.

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 11, Issue 4

Oct. 1992

118 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/146443

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 1992

Published in TOG Volume 11, Issue 4

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https://dl.acm.org/doi/10.1145/3551389
Pasumurthi VAyyappa Ramineni GKumar Jatoth R(2023)Enhanced Image Quantization Through Hybrid Sine Cosine Optimization and K-Means Clustering2023 3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET)10.1109/ICEFEET59656.2023.10452222(01-05)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ICEFEET59656.2023.10452222
Su SGu LYang YZhang ZHarada T(2023)Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01104(11987-11997)Online publication date: 1-Oct-2023
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Recommendations

Comparative analysis of the quantization of color spaces on the basis of the CIELAB color-difference formula

Colour quantization with Ant-tree

Color image quantization with peak-picking and color space

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