Two-dimensional linear prediction and its application to adaptive predictive coding of images
This paper summarizes a study on two-dimensional linear prediction of images and its application to adaptive predictive coding of monochrome images. The study was focused on three major areas: two-dimensional linear prediction of images and its performance, implementation of an adaptive predictor and adaptive quantizer for use in image coding, and linear prediction and adaptive predictive coding of density (logarithm of intensity) images. Among the issues investigated are: autoregressive modeling of 2-D image sequences, estimation of the nonzero average bias of the image samples, stability of the inverse prediction error filter, and estimation of the parameters of a 2-D separable linear predictor. The implementation of the adaptive predictor is based on the results of linear predictive analysis. The adaptive quantization of the prediction error signal is done by using a flexible three-level quantizer for code words of fixed or variable length. The above ideas are further applied to density images for exploiting the multiplicative structure of images. The results of this research indicate that by using adaptive prediction and quantization, intensity and density coded images of high quality can be obtained at information rates as low as 0.7 bits/pixel.