
Culture can play a significant role in evaluating image quality. Therefore, this work considered one of the least studied cultural regions of observers, examining the impact of Central Asian culture on image quality evaluation. More specifically, it investigated how they evaluate the quality of contrast-enhanced images. It was found that observer evaluations vary and can be divided into groups. These groups may have their individual preferences for the quality of contrast-enhanced images. Therefore, the personalization factor should be incorporated into the quality evaluation of (contrast-) enhanced images. Furthermore, the results were compared with another population and differences were found in the overall outcomes of the two observer groups. The variations observed could be due to cultural differences. In addition, this study introduced the Central Asian Contrast-Enhanced Image Quality Dataset (CACEIQD). A variety of image quality metrics, including deep learning techniques, were tested on the dataset. The results indicate that the dataset is challenging and highlight an area for metric improvement. This dataset can be helpful for future research in the field of enhanced image quality evaluation.