Abstract
Computational redundancy of an image represents the amount of computations that can be skipped to improve performance. In order to calculate and exploit the computational redundancy of an image, a similarity measure is required to identify similar neighborhoods of pixels in the image. In this paper, we present two similarity measures: a position-invariant histogram-based measure and a rotation-invariant multiresolutional histogrambased measure. We demonstrate that by using the position-invariant and rotation-invariant similarity measures, on average, the computational redundancy of natural images increases by 34% and 28%, respectively, in comparison to the basic similarity measure. The increase in computational redundancy can lead to further performance improvement. For a case study, the average increase in actual speedup is 211% and 35% for position-invariant and rotation-invariant similarity measures, respectively.
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© 2009 Springer-Verlag Berlin Heidelberg
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Khalvati, F., Tizhoosh, H.R., Hajian, A.R. (2009). Increasing Computational Redundancy of Digital Images via Multiresolutional Matching. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_15
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DOI: https://doi.org/10.1007/978-3-642-02611-9_15
Publisher Name: Springer, Berlin, Heidelberg
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