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The processing of compressed data generally yields decreased computational cost, due to the requirement of fewer operations in the presence of fewer data. We have previously shown that the automated recognition of small targets in compressed imagery, called compressive ATR, is feasible for a variety of compressed image formats. For example, images having domain size X , when tesselated into k X l-pixel blocks and processed by a block-compression transform such as vector quantization (VQ), have compressed domain size V equals X kl pixels. The characterization of each k X l pixel block in terms of one or more parameters of a compressed pixel facilitates the processing of O(Y) such pixels in O(CRD) equals O(X/Y) equals O(kl) work, where CRD denotes the domain compression ratio. In practice, typical computational efficiencies of approximately one-half the domain compression ratio have been realized via processing imagery compressed by VQ, block truncation coding (BTC), and visual pattern image coding (VPIC). In this paper, we extend our previous research in compressive ATR to include the processing of compressed stereoscopic imagery. We begin with a brief review of stereo vision and the correspondence problem, as well as theory fundamental to the processing of compressed data. We then summarize VQ, BTC, and VPIC compression. In Part 2 of this series, we map a cepstrum- based stereo matching algorithm to stereoscopic images represented by the aforementioned compressive formats. Analyses emphasize computational cost and stereo disparity error. Algorithms are expressed in terms of image algebra, a rigorous, concise notation that unifies linear and nonlinear mathematics in the image domain. Since image algebra has been implemented on numerous sequential and parallel computers, our algorithms are feasible and widely portable.
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