Title :
On 2-D recursive LMS algorithms using ARMA prediction for ADPCM encoding of images
Author :
Chung, Young-Sik ; Kanefsky, Morton
Author_Institution :
Dept. of Electr. Eng., Pittsburgh Univ., PA, USA
fDate :
7/1/1992 12:00:00 AM
Abstract :
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
Keywords :
encoding; filtering and prediction theory; least squares approximations; picture processing; pulse-code modulation; 2D linear predictor; ADPCM encoding; ARMA prediction; adaptive differential pulse code modulation; autoregressive moving average; bias term; convergence factors; image encoding; nonnegative images; real time; recursive LMS algorithms; spatial domain; stability control factor; updating algorithm; Convergence; Image coding; Image reconstruction; Least squares approximation; Modulation coding; Poles and zeros; Prediction algorithms; Pulse modulation; Quantization; Two dimensional displays;
Journal_Title :
Image Processing, IEEE Transactions on
