Historically, the data compression techniques utilized to process image data have been Unitary Transform encoding or time domain encoding. Recently, these two approaches have been combined into a hybrid transform domain time domain system. The hybrid system incorporates some of the advantages of both concepts and eliminates some of the disadvantages of each. However, the problems of picture statistics dependence and error propagation still exist. This is due to the fact that the transformed coefficients are non-stationary processes, which implies that a constant DPCM coefficient set cannot be optimal for all scenes. In this paper, an approach is suggested that has the potential of eliminating or greatly alleviating these problems. The approach utilizes modern adaptive estimation and identification theory techniques to "learn" the picture statistics in real time so that an optimal set of coefficients can be identified as the signal statistics change. In this way, the dependency of the system on the picture statistics is greatly reduced. Furthermore, by updating and transmitting a new set of predictor coefficients periodically, the channel error propagation problem is alleviated.