It is well known that one of the problems of the current method for discomfort glare evaluation, called the unified glare rating, is the non-uniform luminance of the glare source. This paper addresses this issue by considering the spatial contrast of luminance as a measure of non-uniformity. An image-based metric is proposed to evaluate discomfort glare by modeling the neural response of human vision. The model takes an absolute luminance image as input and predicts visual discomfort based on the spatial distribution of the luminance of the stimulus and the background. The developed model was tested to predict subjective glare ratings based on an experiment conducted using non-uniform LED sources with symmetric and asymmetric patterns of LEDs, and its performance was compared with the unified glare rating. As expected, the unified glare rating predictions correlated well with the subjective glare evaluations of luminaires with symmetric patterns of LEDs (as they appear less non-uniform) but not for those with asymmetric patterns. Results showed that the developed model, named the Neural Response-based Glare Model, gave similar performance to unified glare rating for symmetric patterns but outperformed UGR for asymmetric patterns of LEDs.

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High Dynamic Range (HDR) imaging applications have been commonly placed recently. Several tone mapping operators (TMOs) have been developed which project the HDR radiance range to that of a display. Currently, there is no agreement on a technique for evaluation of tone mapping operators. The goal of this study is to establish a method based on reference images to evaluate the TMOs. Two psychophysical experiments were carried out for the evaluation of tone mapping operators. In the first experiment, a set of high quality images were generated to possess right extents of image features including contrast, colourfulness and sharpness. These images were further used in the second experiment as reference images to evaluate different TMOs. It was found Reinhard's photographic reproduction based on local TMO gave an overall better performance. CIELAB(2:1) and S- CIELAB metrics were also used to judge colour image quality of the same TMOs. It was found that both metrics agreed well with the visual results.

Tone-mapping operators transform high dynamic range (HDR) images into displayable low dynamic range (LDR) images. Image quality evaluation of these LDR images is not possible by comparison with their corresponding high dynamic range images. Hence, a no-reference image quality metric for tone-mapped LDR images is proposed based on the fitting to the present psychophysical results including different visual image quality attributes. Ten images, including HDR natural scenes, were tonemapped using six TMOs. They were used in the assessment and visual attributes were determined to predict the quality of these images. The visual attributes (brightness and Naturalness) were modeled using parameters derived from CAM16-UCS. Results showed that the quality prediction of the model had a reasonable degree of accuracy.

The computation of perceived attractiveness from facial images has long been a research topic. Many models have been developed to predict the attractiveness of the face from the individual models that have been used to describe the geometry of the face (symmetry, golden ratio and neoclassical canons, according to artists from the Middle Ages, and a combination of the three). An experiment was conducted based on Oriental and South Asian ethnic groups, represented by Chinese and Pakistani facial images. Visual assessments of perceived attractiveness were carried out using a 6-point categorical scale and the results were used to derive a new set of facial feature ratios that maximized the perceived attractiveness of the two ethnic groups. The results were also used to develop a new polynomial model of attractiveness, and to test four existing models. The new model performed the best for Oriental faces. The new model was also best for South Asian faces together with the combined model. Ethnic group differences did not have a significant impact on the perceived attractiveness of the two groups. A set of new facial ratios for the two ethnic groups was determined to maximise attractiveness.