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Proceedings Paper

Two algorithms for compressing noise like signals
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Paper Abstract

Compression is a technique that is used to encode data so that the data needs less storage/memory space. Compression of random data is vital in case where data where we need preserve data that has low redundancy and whose power spectrum is close to noise. In case of noisy signals that are used in various data hiding schemes the data has low redundancy and low energy spectrum. Therefore, upon compressing with lossy compression algorithms the low energy spectrum might get lost. Since the LSB plane data has low redundancy, lossless compression algorithms like Run length, Huffman coding, Arithmetic coding are in effective in providing a good compression ratio. These problems motivated in developing a new class of compression algorithms for compressing noisy signals. In this paper, we introduce a two new compression technique that compresses the random data like noise with reference to know pseudo noise sequence generated using a key. In addition, we developed a representation model for digital media using the pseudo noise signals. For simulation, we have made comparison between our methods and existing compression techniques like Run length that shows the Run length cannot compress when data is random but the proposed algorithms can compress. Furthermore, the proposed algorithms can be extended to all kinds of random data used in various applications.

Paper Details

Date Published: 25 May 2005
PDF: 10 pages
Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); doi: 10.1117/12.603797
Show Author Affiliations
Sos S. Agaian, Univ. of Texas at San Antonio (United States)
Ravindranath Cherukuri, Univ. of Texas at San Antonio (United States)
David Akopian, Univ. of Texas at San Antonio (United States)

Published in SPIE Proceedings Vol. 5809:
Signal Processing, Sensor Fusion, and Target Recognition XIV
Ivan Kadar, Editor(s)

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