Transcription of MULTI-SCALE STRUCTURAL SIMILARITY FOR IMAGE QUALITY ...
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MULTI-SCALE STRUCTURAL SIMILARITY FOR IMAGE QUALITY ...
Published in: Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Nov. 9-12, 2003. IEEE. MULTI-SCALE STRUCTURAL SIMILARITY FOR IMAGE QUALITY ASSESSMENT. Zhou Wang1 , Eero P. Simoncelli1 and Alan C. Bovik2. (Invited Paper). 1. Center for Neural Sci. and Courant Inst. of Math. Sci., New York Univ., New York, NY 10003. 2. Dept. of Electrical and Computer Engineering, Univ. of Texas at Austin, Austin, TX 78712. Email: ABSTRACT stimuli. Thus, these approaches must rely on a number of strong assumptions and generalizations [4],[5]. Furthermore, as the num- The STRUCTURAL SIMILARITY IMAGE QUALITY paradigm is based on the ber of HVS features has increased, the resulting QUALITY assessment assumption that the human visual system is highly adapted for systems have become too complicated to work with in real-world extracting STRUCTURAL information from the scene, and therefore a applications, especially for algorithm optimization purposes.
allP. j ’s. In addition, we normalize the cross-scale settings such that. M j =1 ° j =1. This makes different parameter settings (including all single-scale and multi-scale settings) comparable. The remain-ing job is to determine the relative values across different scales. Conceptually, this should be related to the contrast sensitivity func-
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