Visual model of human blur perception for scene adaptive capturing

Abstract

Despite fast spreading of digital cameras, many people cannot take pictures of high quality, they want, due to lack of photography. To help users under the unfavorable capturing environments, e.g. 'Night', 'Backlighting', 'Indoor', or 'Portrait', the automatic mode of cameras provides parameter sets by manufactures. Unfortunately, this automatic functionality does not give pleasing image quality in general. Especially, length of exposure (shutter speed) is critical factor in taking high quality pictures in the night. One of key factors causing this bad quality in the night is the image blur, which mainly comes from hand-shaking in long capturing. In this study, to circumvent this problem and to enhance image quality of automatic cameras, we propose an intelligent camera processing core having BASE (Scene Adaptive Blur Estimation) and VisBLE (Visual Blur Limitation Estimation). SABE analyzes the high frequency component in the DCT (Discrete Cosine Transform) domain. VisBLE determines acceptable blur level on the basis of human visual tolerance and Gaussian model. This visual tolerance model is developed on the basis of human perception physiological mechanism. In the experiments proposed method outperforms existing imaging systems by general users and photographers, as well.