Quality assessment of JPEG-distorted face images: Influence of affective content

In this work we investigate if the affective content of images influences the perception of image quality. Two database are generated and psychophysical experiments are conducted, where participants rate the stimuli in a five point Likert scale. We have fixed the semantic content, choosing only close-ups of face images, two emotion categories (happy and sad images) and JPEG-distortion. Also the influence of the background is considered. From the analysis of the subjective data we observe that the influence of affective content is more evident for images of very high or very low quality. The subjective scores are further used as ground-truth labels to train a five quality-class classifier. Two different feature spaces are used (visual features and quality metrics) to train a SVM classifier.