IEEE International Conference on Acoustics, Speech & Signal Processing, vol. II, pp. 573-576, Philadelphia, PA, March 2005


Translation Insensitive Image Similarity in Complex Wavelet Domain

Zhou Wang  and  Eero P. Simoncelli

Laboratory for Computational Vision, New York University, New York, NY 10003

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PDF file for ICASSP poster (2.34M)

Abstract: We propose a complex wavelet domain image similarity measure, which is simultaneously insensitive to luminance change, contrast change and spatial translation. The key idea is to make use of the fact that these image distortions lead to consistent magnitude and/or phase changes of local wavelet coefficients. Since small scaling and rotation of images can be locally approximated by translation, the proposed measure also shows robustness to spatial scaling and rotation when these geometric distortions are small relative to the size of the wavelet filters. Compared with previous methods, the proposed measure is computationally efficient, and can evaluate the similarity of two images without a precise registration process at the front end.