Definition
Structural similarity is a quality metric for range images that can handle missing data. It is based on the Multi Scale Structural Similarity Index.
Introduction
The R-SSIM Index uses of a modified version of the Multi-Scale SSIM [1] Index, but specially designed for range images. Range images, bear both many similarities and differences with luminance images. When applying the MS-SSIM algorithm to range images, the three similarity components of MS-SSIM, that is, luminance, contrast and structure, find their counterparts in the range domain as elevation, surface roughness and 3-D structure.
MS-SSIM
The MS-SSIM Index utilizes the same basic algorithm as the SSIM Index first proposed in [2], except it functions over several scales which allows the algorithm to incorporate image details at different resolutions. The algorithm’s greatest appeal is that it matches human subjectivity, since it is...
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References
Z. Wang, E.P. Simoncelli, and A.C. Bovik, “Multi-Scale Structural Similarity for Image Quality Assessment,” Proceedings of the IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2003.
Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, Vol. 13, April 2004, pp. 600–612.
M. Hebert, “Active and Passive Range Sensing for Robotics,” Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC, 2000.
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© 2008 Springer-Verlag
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Malpica, W., Bovik, A. (2008). Range Image Quality Assessment by Structural Similarity. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78414-4_80
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DOI: https://doi.org/10.1007/978-0-387-78414-4_80
Publisher Name: Springer, Boston, MA
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