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Image quality assessment using edge and contrast similarity

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3 Author(s)
Wei Fu ; Dept. of Electron. Eng., Fudan Univ., Shanghai ; Xiaodong Gu ; Yuanyuan Wang

Measurement of visual quality is of fundamental importance to some image processing applications. And the perceived image distortion of any image strongly depends on the local features, such as edges, flats and textures. Since edges often convey much information of an image, we propose a novel algorithm for image quality assessment based on the edge and contrast similarity between the distorted image and the reference(perfect) image. We demonstrate its promise through a set of intuitive examples, as well as validate its performance with subjective ratings. We also compare our method with two other state-of-the-art objective ones, which uses 550 images with different distortion types and BP neural network.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008