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Proceedings Paper

Introduction to the recognition of patterns in compressed data: image template operations over block-, transform-, runlength-encoded, and vector-quantized data
Author(s): Mark S. Schmalz
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Paper Abstract

The processing of compressed or encrypted imagery is a vital new area of research that can achieve computational efficiency and data security by processing fewer data, which may be obscurely encoded. In particular, we have derived numerous image processing algorithms that achieve computational speedups which approach the compression ratio (CR). In this paper, we extend our previous work in computation and pattern recognition over one-dimensional compressed data to include operations over multidimensional imagery. We discuss the processing of transform, block, and runlength encoded imagery, as well as the special case of vector-quantized (VQ) imagery. We note that certain cases of template matching over the range space of the block-encoding or VQ transform can yield a computational speedup that approaches the domain compression ratio (CRd). Defined as the ratio of the number of source data to the number of compressed data, CRd generally exceeds the customary compression ratio. Analyses emphasize computational complexity, information loss, and implementational feasibility.

Paper Details

Date Published: 5 July 1995
PDF: 14 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213023
Show Author Affiliations
Mark S. Schmalz, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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