Share Email Print

Proceedings Paper

General theory for the processing of compressed and encrypted imagery with taxonomic analysis
Author(s): Mark S. Schmalz
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The processing of compressed and encrypted imagery can exhibit advantages of computational efficiency as well as data security. Due to the data reduction inherent in compression, the computational speedup achieved via compressive processing can equal or exceed the compression ratio. Encryptive transformations which yield compressed ciphertext can similarly facilitate computational speedup, and are well known. However, due to analytical difficulties inherent in the derivation of operations which compute over the range space of commonly employed compressive transforms, reports of such processing paradigms are not evident in the open literature. In this introductory paper, we describe compressive and encryptive transformations in terms of functional mappings derived from abstract mathematics and image algebra (IA). An emerging technology, IA is a rigorous, concise notation which unifies linear and nonlinear mathematics in the image domain, and has been implemented on a variety of serial and parallel computers. Additionally, we derive a taxonomy of image transformations. Each taxonomic class is analyzed in terms of computational complexity and applicability to image and signal processing. We further present decompositions specific to each transformational class, which facilitate the design of operations over a given transform's range space. Examples and analysis are given for several image operations.

Paper Details

Date Published: 1 July 1992
PDF: 14 pages
Proc. SPIE 1702, Hybrid Image and Signal Processing III, (1 July 1992); doi: 10.1117/12.60566
Show Author Affiliations
Mark S. Schmalz, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 1702:
Hybrid Image and Signal Processing III
David P. Casasent; Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top