Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2015

Abstract

Image decolorization is a fundamental problem for many real-world applications, including monochrome printing and photograph rendering. In this paper, we propose a new color-to-gray conversion method that is based on a region-based saliency model. First, we construct a parametric color-to-gray mapping function based on global color information as well as local contrast. Second, we propose a region-based saliency model that computes visual contrast among pixel regions. Third, we minimize the salience difference between the original color image and the output grayscale image in order to preserve contrast discrimination. To evaluate the performance of the proposed method in preserving contrast in complex scenarios, we have constructed a new decolorization data set with 22 images, each of which contains abundant colors and patterns. Extensive experimental evaluations on the existing and the new data sets show that the proposed method outperforms the state-of-the-art methods quantitatively and qualitatively.

Keywords

Contrast discrimination, Contrast enhancement, Conversion methods, Dimensionality reduction, Experimental evaluation, Global color information, Gray-scale images, State-of-the-art methods

Discipline

Databases and Information Systems

Research Areas

Information Systems and Management

Publication

IEEE Transactions on Image Processing

Volume

24

Issue

1

First Page

434

Last Page

443

ISSN

1057-7149

Identifier

10.1109/TIP.2014.2380172

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TIP.2014.2380172

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