A no-reference image quality assessment

Abstract

This paper presents a no-reference image quality assessment, targeted towards blur distortions based on the study of human blur perception for varying contrast values. A probabilistic framework is developed based on the sensitivity of human blur perception at different contrasts. Utilizing this framework, the probability of detecting blur at each edge in an image is estimated. The blur perception information at each edge is then pooled over the entire image to obtain a final quality score by evaluating the cumulative probability of blur detection. Proposed metric is able to predict relative amount of blurriness in images. Higher metric value represent less blurred image. Results are provided to illustrate the performance of proposed metric. Performance of proposed metric is compared with existing no reference image quality metric for various publically available image databases.