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Image compression using vector quantization of linear (one- step) prediction errors

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3 Author(s)
Mathews, V.John ; University of Utah, Salt Lake City, Utah ; Waite, R. ; Thao Tran

A novel approach to image compression using vector quantization of linear (one-step) prediction errors is presented in this paper. In order to minimize the image reconstruction error, we choose the optimum predictor coefficients (in a least-squares sense) that satisfy the additional constraint that the energy of the impulse response function of the inverse reconstruction filter is bounded by a small constant c. Further, the code vectors are selected such that the reconstruction error is minimized, rather than the quantization noise for the prediction error sequences. Examples demonstrating the excellent quality of the reconstructed images using our approach at bit rates below 0.65 bit/pixel are presented.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '87.  (Volume:12 )

Date of Conference:

Apr 1987

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