Memoization-based high-performance video frame processing

Abstract

We present a high-performance improvement method for implementation of local processing algorithms for video frames by using the benefit of memoization technique. Memoization is a technique that uses the advantage of data redundancy to minimize the amount of computations performed by retrieving previous results instead of computing again, which leads to faster processing speed. In this method, the benefit of interframe redundancy in adjacent frames is used for memoizing where the pixels in a sequence of frames are correlated. We have developed this method in software and applied it to edge detection and median filters. The typical speedups achieved in the median filter range from 2.2× with exact results to 6.48× in tolerant methods and in edge detection filter range from 2.1× with exact results to 5.41× in tolerant method. The structural similarity index metric that is used for evaluating the perceived similarity of the tolerant result with ideal result was applied to each adjacent frame in sample stream frames. The typical values of this parameter were 0.96 in median filter and 0.71 in edge detection filter.