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Proceedings Paper

Wavelet-based pavement image compression and noise reduction
Author(s): Jian Zhou; Peisen S. Huang; Fu-Pen Chiang
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Paper Abstract

For any automated distress inspection system, typically a huge number of pavement images are collected. Use of an appropriate image compression algorithm can save disk space, reduce the saving time, increase the inspection distance, and increase the processing speed. In this research, a modified EZW (Embedded Zero-tree Wavelet) coding method, which is an improved version of the widely used EZW coding method, is proposed. This method, unlike the two-pass approach used in the original EZW method, uses only one pass to encode both the coordinates and magnitudes of wavelet coefficients. An adaptive arithmetic encoding method is also implemented to encode four symbols assigned by the modified EZW into binary bits. By applying a thresholding technique to terminate the coding process, the modified EZW coding method can compress the image and reduce noise simultaneously. The new method is much simpler and faster. Experimental results also show that the compression ratio was increased one and one-half times compared to the EZW coding method. The compressed and de-noised data can be used to reconstruct wavelet coefficients for off-line pavement image processing such as distress classification and quantification.

Paper Details

Date Published: 17 September 2005
PDF: 11 pages
Proc. SPIE 5914, Wavelets XI, 59141Z (17 September 2005); doi: 10.1117/12.612926
Show Author Affiliations
Jian Zhou, SUNY at Stony Brook (United States)
Peisen S. Huang, SUNY at Stony Brook (United States)
Fu-Pen Chiang, SUNY at Stony Brook (United States)


Published in SPIE Proceedings Vol. 5914:
Wavelets XI
Manos Papadakis; Andrew F. Laine; Michael A. Unser, Editor(s)

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