Complexity-distortion tradeoffs in vector matching based on probabilistic partial distance techniques

We consider the problem of searching for the best match for an input among a set of vectors, according to some predetermined metric. Examples of this problem include the search for the best match for an input in a VQ encoder and the search for a motion vector in motion estimation-based video coding. We propose an approach that computes a partial distance metric and uses prior probabilistic knowledge of the reliability of the estimate to decide on whether to stop the distance computation. This is achieved with a simple hypothesis testing and the result, an extension of the partial distance technique of Bei and Gray (1985) provides additional computation savings at the cost of a (controllable) loss in matching performance.