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

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2 Author(s)
Y. -S. Chung ; Dept. of Electr. Eng., Pittsburgh Univ., PA, USA ; M. Kanefsky

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

Published in:

IEEE Transactions on Image Processing  (Volume:1 ,  Issue: 3 )