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Proceedings Paper

Color correction optimization with hue regularization
Author(s): Heng Zhang; Huaping Liu; Shuxue Quan
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Paper Abstract

Previous work has suggested that observers are capable of judging the quality of an image without any knowledge of the original scene. When no reference is available, observers can extract the apparent objects in an image and compare them with the typical colors of similar objects recalled from their memories. Some generally agreed upon research results indicate that although perfect colorimetric rendering is not conspicuous and color errors can be well tolerated, the appropriate rendition of certain memory colors such as skin, grass, and sky is an important factor in the overall perceived image quality. These colors are appreciated in a fairly consistent manner and are memorized with slightly different hues and higher color saturation. The aim of color correction for a digital color pipeline is to transform the image data from a device dependent color space to a target color space, usually through a color correction matrix which in its most basic form is optimized through linear regressions between the two sets of data in two color spaces in the sense of minimized Euclidean color error. Unfortunately, this method could result in objectionable distortions if the color error biased certain colors undesirably. In this paper, we propose a color correction optimization method with preferred color reproduction in mind through hue regularization and present some experimental results.

Paper Details

Date Published: 25 January 2011
PDF: 9 pages
Proc. SPIE 7866, Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications, 786610 (25 January 2011); doi: 10.1117/12.871977
Show Author Affiliations
Heng Zhang, Oregon State Univ. (United States)
Broadcom Corp. (United States)
Huaping Liu, Oregon State Univ. (United States)
Shuxue Quan, Broadcom Corp. (United States)


Published in SPIE Proceedings Vol. 7866:
Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications
Reiner Eschbach; Gabriel G. Marcu; Alessandro Rizzi, Editor(s)

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