37^th IEEE Asilomar Conference on Signals, Systems and Computers, Nov. 2003

Multi-scale Structural Similarity for Image Quality Assessment

Zhou Wang¹, Eero P. Simoncelli¹ and Alan C. Bovik²

(Invited Paper)

¹Laboratory for Computational Vision (LCV), New York University, New York, NY 10003

²Laboratory for Image and Video Engineering (LIVE), The University of Texas at Austin, Austin, TX 78712

PDF File (492K)

Abstract: The structural similarity image quality assessment approach is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a novel multi-scale structural similarity method, which supplies more flexibility than single-scale methods in incorporating the variations of image resolution and viewing condition. Experimental comparisons demonstrate the effectiveness of the proposed method.