NDLI: Segmentation based linear predictive coding of multispectral images

Content Provider	IEEE Xplore Digital Library
Author	Jian-Hong Hu Yao Wang Cahill, P.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. Eng., Polytechnic Univ., Brooklyn, NY, USA (Jian-Hong Hu; Yao Wang)
Abstract	This paper presents a segmentation based linear predictive coding (SLPC) method for multispectral images. Given a set of multispectral images, the SLPC method first segments it into statistically distinct regions. It then finds a suitable linear prediction model for each region. Finally, it quantizes the prediction error in each class using a vector quantizer. The original image set is described by the segmentation map, the model parameters for each class, and the quantized prediction errors. The SLPC method can produce very high compression gains, because the specification of the segmentation map and model parameters requires significantly fewer bits than that for the original intensity values. This method has been applied to magnetic resonance head images with three spectral bands (one T1 weighted and two T2 weighted, 256/spl times/256/spl times/12 bits/image). Images compressed by a factor of more than 22 have been regarded as indistinguishable from the originals, by several radiologists.
Starting Page	721
Ending Page	725
File Size	460092
Page Count	5
File Format	PDF
ISBN	0818669527
DOI	10.1109/ICIP.1994.413794
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-11-13
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Linear predictive coding Multispectral imaging Autoregressive processes Image coding Predictive models Biomedical imaging Head Radiology Educational institutions
Content Type	Text
Resource Type	Article

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