An Image-Quality Assessment Algorithm for Solar Tone-Mapped Images Based on Visual Simulation

To facilitate the display of solar images, captured solar images are often subjected to tone-mapping and enhancement. Accordingly, it is necessary to assess the quality of solar images before and after tone-mapping. However, there exist certain differences in human subjective perception under different ambient light intensities and when using different displays. Therefore, this paper proposes an image-quality assessment algorithm for solar tone-mapped images based on visual simulation. By effectively modeling the display characteristics model and the human visual system (HVS) model, the modeled images can reflect the perceptual effects of the human visual system under different ambient lighting conditions and display devices. Feeding modeled images into general image-quality assessment (IQA) metrics enables a better alignment with human visual perception. The proposed approach has been validated by two metrics: the solar IQA metric T based on the image power spectrum, and the IQA metric S based on the signal detection probability. We conducted subjective quality assessment experiments in a bright indoor environment with an ambient light intensity of 400 lux. By adjusting the display brightness, the Ambient Contrast Ratio (ACR) was controlled at 226.69 and 17.83, respectively. When the ACR was 226.69, the subjective Spearman Rank Correlation Coefficient (SRCC) of the T metric for the input before and after modeled increased by 1.83%, and that of the S metric by 5.44%. In addition, at an ACR of 17.83, the subjective SRCC of the metric T increased by 1.79%, while that of the metric S by 8.38%. We also conducted a regression test on the tone-mapping enhancement parameters using the metric S, and the test results demonstrated that the images generated from the metric with modeled image input yielded better visual effects.