Introduction to the recognition of patterns in compressed data: image template operations over block-, transform-, runlength-encoded, and vector-quantized data

Mark S. Schmalz

doi:10.1117/12.213023

5 July 1995 Introduction to the recognition of patterns in compressed data: 1. image template operations over block-, transform-, runlength-encoded, and vector-quantized data

Mark S. Schmalz

Author Affiliations +

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213023
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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 (CR_d). Defined as the ratio of the number of source data to the number of compressed data, CR_d generally exceeds the customary compression ratio. Analyses emphasize computational complexity, information loss, and implementational feasibility.

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Mark S. Schmalz "Introduction to the recognition of patterns in compressed data: 1. image template operations over block-, transform-, runlength-encoded, and vector-quantized data", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213023

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