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Proceedings Paper

The effect of lightness scaling on the perceived color quality of compressed digital videos
Author(s): Chin Chye Koh; John M. Foley; Sanjit K. Mitra
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Paper Abstract

In this work, we studied how video compression and lightness scaling interact to affect the overall video quality and the color quality attributes. We examined three subjective attributes: perceived color preference, perceived color naturalness, and overall annoyance as digital videos were subjected to compression and lightness scaling. Psychophysical experiments were carried out in which naïve subjects made numerical judgments of the three subjective attributes. We found that preference and naturalness scores are concave down functions of mean lightness with an associated maximum, while annoyance scores are concave up with an associated minimum. As compression increases, both preference and naturalness scores decrease and vary less with mean lightness. Maximum preference, naturalness, and annoyance scores generally occur at similar mean lightness values. Preference, naturalness, and annoyance scores for individual videos, are approximated relatively well by Gaussian functions of mean lightness. Preference and naturalness scores decreases while annoyance scores increase as an S-shaped function of the logarithm of the total squared error. A three-parameter model is shown to provide a good description of how each attribute depends on lightness and compression for individual videos. Model parameters vary with video content.

Paper Details

Date Published: 14 February 2008
PDF: 12 pages
Proc. SPIE 6806, Human Vision and Electronic Imaging XIII, 680612 (14 February 2008); doi: 10.1117/12.767047
Show Author Affiliations
Chin Chye Koh, Video Products Group, Inc. (United States)
John M. Foley, Univ. of California, Santa Barbara (United States)
Sanjit K. Mitra, Univ. of California, Santa Barbara (United States)

Published in SPIE Proceedings Vol. 6806:
Human Vision and Electronic Imaging XIII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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