A content-based image quality metric

Authors:
Xinbo Gao;Tao Wang;Jie Li
Affiliations:
School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China;School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China;School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Year:
2005

Citing 3
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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.