DocumentCode :
1084558
Title :
Reversible image compression bounded by noise
Author :
Roger, R.E. ; Arnold, John F.
Author_Institution :
Dept. of Electr. Eng., New South Wales Univ., Kensington, NSW, Australia
Volume :
32
Issue :
1
fYear :
1994
fDate :
1/1/1994 12:00:00 AM
Firstpage :
19
Lastpage :
24
Abstract :
Reversible image compression rarely achieves compression ratios larger than about 3:1. An explanation of this limit is offered, which hinges upon the additive noise the sensor introduces into the image. Simple models of this noise allow lower bounds on the bit rate to be estimated from sensor noise parameters rather than from ensembles of typical images. The model predicts that an 8-b single-band image subject to noise with unit standard deviation can be compressed reversibly to no less than 2.0 b/pixel, equivalent to a maximum compression ratio of about 4:1. The model has been extended to multispectral imagery. The Airborne Visible and Infra Red Imaging Spectrometer (AVIRIS) is used as an example, as the noise in its 224 bands is well characterized. The model predicts a lower bound on the bit rate for the compressed data of about 5.5 b/pixel when a single codebook is used to encode all the bands. A separate codebook for each band (i.e., 224 codebooks) reduces this bound by 0.5 b/pixel to about 5.0 b/pixel, but 90% of this reduction is provided by only four codebooks. Empirical results corroborate these theoretical predictions
Keywords :
data compression; geophysical techniques; geophysics computing; image coding; image processing; remote sensing; AVIRIS; Airborne Visible and Infra Red Imaging Spectrometer; bit rate; bounded by noise; codebook; data compression ratio; digital imagery; geophysical measurement technique; image coding; land surface imaging; limit; multispectral imagery; remote sensing; reversible image compression; sensor; Additive noise; Bit rate; Fasteners; Image coding; Image sensors; Multispectral imaging; Optical imaging; Pixel; Predictive models; Signal to noise ratio;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/36.285185
Filename :
285185
Link To Document :
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