A content-based image quality metric

Abstract

Image quality assessment plays an important role in relevant fields of image processing. The traditional image quality metric, such as PSNR, cannot reflect the visual perception to the image effectively. For this purpose, based on the fuzzy Sugeno integral a novel image quality assessment measure, called content-based metric (CBM), is proposed in this paper. It fuses the amount and local information into the similarity of the image structural information and gives a comprehensive evaluation for the quality of the specified image. The experimental results illustrate that the proposed metric has a good correlation with the human subjective perception, and can reflect the image quality effectively. © Springer-Verlag Berlin Heidelberg 2005.