Color quantization method based on principal component analysis and linear discriminant analysis for palette-based image generation

High performance of color quantization processing is very important for obtaining limited-color images with good quality. The median cut algorithm (MCA) is a typical color quantization method. Its computational cost is low owing to its simple algorithm, but the quality of output images is mediocre a...

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Bibliographic Details
Published in:Optical review (Tokyo, Japan) Japan), 2017-12, Vol.24 (6), p.741-756
Main Authors: Ueda, Yoshiaki, Koga, Takanori, Suetake, Noriaki, Uchino, Eiji
Format: Article
Language:English
Subjects:
Atomic
Lasers
Microwaves
Molecular
Optical and Plasma Physics
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
Quantum Optics
Regular Paper
RF and Optical Engineering
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Summary:High performance of color quantization processing is very important for obtaining limited-color images with good quality. The median cut algorithm (MCA) is a typical color quantization method. Its computational cost is low owing to its simple algorithm, but the quality of output images is mediocre at best. In this paper, we describe a modification of MCA. In our method, we use a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) to accomplish effective partitioning of color space. Concretely, PCA and LDA are used to calculate partitioning planes and their positions, respectively. We verify the effectiveness of our method through experiments using 24-bit full-color natural images.
ISSN:1340-6000
1349-9432
DOI:10.1007/s10043-017-0376-1