Some experimental results in adaptive prediction DPCM coding of images

In this paper we present results from our work on adaptive prediction dpcm for grey level images using the doubly stochastic Gaussian image model. This model consists of a lower level level 2-D Markov chain which takes on L different values and correspondingly L different sets of predictor parameters for an upper level conditionally Gaussian field. A simple gradient based algorithm which uses fuzzy decision theory is used to identify the lower level chain from observations on the upper level field, i.e. the actual image. The upper level field is encoded by 2-D DPCM using spatially varying predictors as determined by the lower level chain. For our simulations L was chosen to be 5, with 4 models representing edges at 0, 45, 90, and 135 degrees and the fifth model representing the non-edge regions. Both fixed and adaptive (Jayant type) quantizers were used. Greater compression is achieved by subsampling in the non-edge regions of the image and then interpolating at the decoder.