Processing of compressed imagery: application to morphological and gray-scale computations over transform-, block-, runlength-, and derivative-encoded imagery

Mark S. Schmalz

doi:10.1117/12.60546

1 July 1992 Processing of compressed imagery: application to morphological and gray-scale computations over transform-, block-, runlength-, and derivative-encoded imagery

Mark S. Schmalz

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Proceedings Volume 1702, Hybrid Image and Signal Processing III; (1992) https://doi.org/10.1117/12.60546
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

The processing of compressed imagery exhibits computational advantages due to the processing of fewer data, as well as the advantage of low-level data security afforded by the encoding format. We have elsewhere discussed the general theory of compressive processing, and have presented complexity analyses which support the claim of computational speedup. In this introductory paper, general methods are described for the processing of signals and imagery encoded via transform-, block-, runlength-, and derivative-coding schemes. Operations of interest include unary and binary pointwise operations, the global reduce operation, and neighborhood operations, such as morphological erosion and dilation. Implementational analyses emphasize the relationship between the compression ratio and the time complexity of compressive computation.

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Mark S. Schmalz "Processing of compressed imagery: application to morphological and gray-scale computations over transform-, block-, runlength-, and derivative-encoded imagery", Proc. SPIE 1702, Hybrid Image and Signal Processing III, (1 July 1992); https://doi.org/10.1117/12.60546

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KEYWORDS

Image compression

Image processing

Computer programming

Signal processing

Binary data

Chromium

Silicon

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