Optimization of optical flow for scalable coding

Optical flow or dense motion field representations provide an alternative to block based schemes and are capable of describing smooth motion flows without introducing any artificial block boundaries. However dense motion fields have not been widely adopted for video coding applications principally due to the prohibitive cost of communicating such detailed motion representations. In this paper our focus is on estimating dense motion fields subject to R-D constraints derived from wavelet based coding of the motion data. We develop a probabilistic framework for estimating dense motion fields that is capable of evaluating the tradeoff between sparsity in the transformed domain and motion compensated distortion. R-D results show that we are able to improve upon traditional optical flow descriptions.