Evaluation of quality measures for color quantization

The visual quality evaluation is one of the fundamental challenging problems in image processing. It plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods centered mainly on images altered by common distortions while paying little attention to the distortion introduced by color quantization. This happens despite there is a wide range of applications requiring color quantization as a preprocessing step since many color-based tasks are more efficiently accomplished on an image with a reduced number of colors. To fill this gap, at least partially, we carry out a quantitative performance evaluation of nine currently widely-used full-reference image quality assessment measures. The evaluation runs on two publicly available and subjectively rated image quality databases for color quantization degradation by considering their appropriate combinations and subparts. The evaluation results indicate what are the quality measures that have closer performances in terms of their correlation to the subjective human rating and prove that the selected image database significantly impacts the evaluation of the quality measures, although a similar trend on each database is maintained. The detected strong trend similarity, both on individual databases and databases obtained by a proper combination, provides the ability to validate the database combination process and 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 appropriate quality measures for color quantization and to improve their future employment.