Introduction to the recognition of patterns in compressed data: optical processing of data transformed by block-, transform-, and runlength-encoding, as well as vector quantization

Mark S. Schmalz

doi:10.1117/12.205780

28 March 1995 Introduction to the recognition of patterns in compressed data: optical processing of data transformed by block-, transform-, and runlength-encoding, as well as vector quantization

Mark S. Schmalz

Author Affiliations +

Proceedings Volume 2490, Optical Pattern Recognition VI; (1995) https://doi.org/10.1117/12.205780
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

We have recently shown that the processing of compressed and encrypted imagery can achieve computational speedup and data security by processing fewer data, which are encoded in an obscure format [5- 9]. Our previous work in compressive processing produced numerous image processing algorithms that yielded computational speedups on the order of the compression ratio. In Part 1 of this series [1], we discuss the theoretical basis for pattern recognition over compressed imagery. In this paper, we present theory in support of optical or electro-optical implementations of convolution or correlation operations over block-, transform-, and runlength-encoded imagery, as well as data encoded by vector quantization (VQ). Unlike our previous work in this area, we do not derive operations that return a compressed result. Instead, our algorithms produce a map of correlation coefficients in the image domain, using a compressed image as input. Several of our architectures could, in principle, perform in time that is at least proportional to the compression ratio. Theory is expressed in terms of image algebra, an emerging branch of mathematics that unifies linear and nonlinear mathematics in the image domain. Image algebra has been implemented on a variety of workstations and parallel processors, as well as electro-optical processors. Thus our algorithms are feasible as well as portable Analyses emphasize computational complexity and information loss.

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Mark S. Schmalz "Introduction to the recognition of patterns in compressed data: optical processing of data transformed by block-, transform-, and runlength-encoding, as well as vector quantization", Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); https://doi.org/10.1117/12.205780

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