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

Color quantization method based on principal component analysis and linear discriminant analysis... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optical Review Springer Journals

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

Loading next page...
 
/lp/springer_journal/color-quantization-method-based-on-principal-component-analysis-and-iZxZRG0KLR
Publisher
Springer Japan
Copyright
Copyright © 2017 by The Optical Society of Japan
Subject
Physics; Optics, Lasers, Photonics, Optical Devices; Atomic, Molecular, Optical and Plasma Physics; Quantum Optics; Microwaves, RF and Optical Engineering
ISSN
1340-6000
eISSN
1349-9432
D.O.I.
10.1007/s10043-017-0376-1
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Optical ReviewSpringer Journals

Published: Oct 4, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off