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Volume: 7 | Article ID: art00057
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Image Dependent Gamma Selection Based on Color Palette Equalization and a Simple Lightness Model
Juha Katajamäki
Pekka Laihanen
  DOI :  10.2352/CIC.1999.7.1.art00057  Published Online :  January 1999
Abstract

A method for estimating the best gamma value for an RGB image is described. The algorithm is intended to be part of a larger image enhancement system. It first quantizes the image colors into a small palette and then finds the gamma that distributes the palette colors most evenly in a modeled perceptual lightness scale, which adapts to the overall image luminance. The algorithm was tested both against gammas assumed to be correct and subjectively optimal gammas. The estimated gamma offers no statistical advantage when a reliable correct gamma is known, but when one is not available, the algorithm can be used to select gammas that correlate with the subjective optimum to a degree comparable to differences between individual subjects.

Journal Title : Color and Imaging Conference
Publisher Name : Society of Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151, USA
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 DOI 10.2352/CIC.1999.7.1.art00057
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  Cite this article 

Juha Katajamäki, Pekka Laihanen, "Image Dependent Gamma Selection Based on Color Palette Equalization and a Simple Lightness Model"  in Proc. IS&T 7th Color and Imaging Conf.,  1999,  pp 301 - 306,  https://doi.org/10.2352/CIC.1999.7.1.art00057

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Copyright © Society for Imaging Science and Technology 1999
72010350
Color and Imaging Conference
color imaging conf
2166-9635
Society of Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151, USA
2166-9635(19990101)1999:1L.301;1-
cic_v1999n1/splitsection57.xml
/ist/cic/1999/00001999/00000001/art00057
Articles
Image Dependent Gamma Selection Based on Color Palette Equalization and a Simple Lightness Model
KatajamäkiJuha
LaihanenPekka
01011999
1999
1
301
306
1999
A method for estimating the best gamma value for an RGB image is described. The algorithm is intended to be part of a larger image enhancement system. It first quantizes the image colors into a small palette and then finds the gamma that distributes the palette colors most evenly in a modeled perceptual lightness scale, which adapts to the overall image luminance. The algorithm was tested both against gammas assumed to be correct and subjectively optimal gammas. The estimated gamma offers no statistical advantage when a reliable correct gamma is known, but when one is not available, the algorithm can be used to select gammas that correlate with the subjective optimum to a degree comparable to differences between individual subjects.