Complex wavelet structural similarity: a new image similarity index

  • Authors:

  • Mehul P. Sampat;Zhou Wang;Shalini Gupta;Alan Conrad Bovik;Mia K. Markey

  • Affiliations:

  • Advanced Imaging in Multiple Sclerosis Laboratory, Department of Neurology, University of California San Francisco, San Francisco, CA;Department of Electrical and Computer Engineering, University of Waterloo, ON, Canada;Laboratory for Image and Video Engineering and the Biomedical Informatics Lab, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX;Laboratory for Image and Video Engineering, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX;Biomedical Informatics Lab, Department of Biomedical Engineering, University of Texas Austin, TX

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2009

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Abstract

We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a pre-processing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.