Predicting the quality of natural images without using a reference image has always been a challenging task. Numerous approaches have been proposed in the past, but they mainly focused on spatial and frequency domain degradations like blur, noise, and compression. Image quality metrics (IQMs) in literature perform with quite a high accuracy for such types of degraded images. However, their performances are not good on the images modified in the color domain. In this study, psychophysical experiments were conducted to assess the quality of the color domain images. A new dataset was developed for this purpose. Additionally, a second dataset consisting of color domain modified images from the three previously published datasets were used in the psychophysical experiments. The newly developed dataset was then used to develop three IQMs based on absolute values, relative values, and statistical analysis of image color appearance attributes. Their performances were then evaluated together with five spatial domain IQMs from the literature using cross-database evaluation methodology. The results showed that the color-domain IQMs outperformed the other models. The absolute and relative attributes-based models, when combined, achieved the best performance. The present results suggest that more effort is needed to improve the performance of color domain IQMs for image quality estimation.
With the advancement of imaging technology, high dynamic range (HDR) images can now be captured and displayed to produce realistic effects. Tone mapping operators (TMOs) are used to map HDR radiance to the displayable range. A reliable TMO would play a significant role in the accurate reproduction of HDR scenes. The present study aimed to establish an image quality metric based on external references to evaluate various TMOs. Two psychophysical experiments were conducted to develop reference images and to investigate the performance of TMOs. In experiment 1, a set of high quality reference images was developed by rendering image features in terms of contrast, sharpness, and colorfulness, to achieve good rendering for each image. The images were used as reference in experiment 2 to evaluate the performance of 14 TMOs using a six-point categorical judgment method. The TMOs were evaluated using four scales, i.e., contrast, sharpness, colorfulness, and overall performance. The hierarchical relationship among TMOs was established. The results were further compared with previous studies, and high correlation was found between the current experiments and previous studies.
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