On 2-D recursive LMS algorithms using ARMA prediction for ADPCM encoding of images

A two-dimensional (2D) linear predictor which has an autoregressive moving average (ARMA) representation well as a bias term is adapted for adaptive differential pulse code modulation (ADPCM) encoding of nonnegative images. The predictor coefficients are updated by using a 2D recursive LMS (TRLMS) a...

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Bibliographic Details
Published in:IEEE transactions on image processing 1992-07, Vol.1 (3), p.416-422
Main Authors: Chung, Y.-S., Kanefsky, M.
Format: Article
Language:English
Subjects:
Convergence
Image coding
Image reconstruction
Least squares approximation
Modulation coding
Poles and zeros
Prediction algorithms
Pulse modulation
Quantization
Two dimensional displays
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Summary:A two-dimensional (2D) linear predictor which has an autoregressive moving average (ARMA) representation well as a bias term is adapted for adaptive differential pulse code modulation (ADPCM) encoding of nonnegative images. The predictor coefficients are updated by using a 2D recursive LMS (TRLMS) algorithm. A constraint on optimum values for the convergence factors and an updating algorithm based on the constraint are developed. The coefficient updating algorithm can be modified with a stability control factor. This realization can operate in real time and in the spatial domain. A comparison of three different types of predictors is made for real images. ARMA predictors show improved performance relative to an AR algorithm.< >
ISSN:1057-7149
1941-0042
DOI:10.1109/83.148614