Fast Image Block Matching for Image Detection and MPEG Encoding Based on Underlying Symmetries

Abstract

Image block matching is one of the representative methods for image detection and motion compensation in MPEG. Block matching between two images is a problem of finding symmetry between two images by matching macro blocks that are symmetrical to each other in two given images. The greater the PSNR value, the greater the symmetry of the two images. In the given two images, the two macro blocks with the minimum matching error values are regarded as symmetrical to each other. The classical method of calculating the matching error function for every pixel in the entire search area and choosing the smallest of them guarantees global convergence but requires a lot of computation, especially for large intensities. For this reason, many sparse search methods have been developed to reduce the amount of computation. In this paper, we introduce a gradient descent vector optimization algorithm with guaranteed global convergence to the image block matching problem by utilizing the conceptual symmetry of the vector optimization problem, which is a continuous variable, and the image block matching problem, which is a discrete variable. By blurring the image, we transform the matching cost function closer to being unimodal so that the descent-type algorithm works well. As a result, although the proposed method is simple, it can reduce the amount of computation remarkably and has more robustness for the large displacement of image blocks compared to existing sparse search methods.