1 research outputs found
Evaluation of quality measures for color quantization
Visual quality evaluation is one of the challenging basic problems in image
processing. It also plays a central role in the shaping, implementation,
optimization, and testing of many methods. The existing image quality
assessment methods focused on images corrupted by common degradation types
while little attention was paid to color quantization. This in spite there is a
wide range of applications requiring color quantization assessment being used
as a preprocessing step when color-based tasks are more efficiently
accomplished on a reduced number of colors. In this paper, we propose and
carry-out a quantitative performance evaluation of nine well-known and commonly
used full-reference image quality assessment measures. The evaluation is done
by using two publicly available and subjectively rated image quality databases
for color quantization degradation and by considering suitable combinations or
subparts of them. The results indicate the quality measures that have closer
performances in terms of their correlation to the subjective human rating and
show that the evaluation of the statistical performance of the quality measures
for color quantization is significantly impacted by the selected image quality
database while maintaining a similar trend on each database. The detected
strong similarity both on individual databases and on databases obtained by
integration provides the ability to validate the integration process and to
consider the quantitative performance evaluation on each database as an
indicator for performance on the other databases. The experimental results are
useful to address the choice of suitable quality measures for color
quantization and to improve their future employment.Comment: Preprin