Implementation of parallel motion estimation model for video sequence compression

Video image compression has been an area where the computational demand is far above the capacity of conventional sequential processing. In this paper, we present a parallel motion estimation model for video sequence compression using distributed computing on a local network. The approach proposed is the decomposition of functions and data on a cluster of workstations using MPI mechanism. Parallel compression is achieved by having a multiple networked personal computer systems that perform compression on different chunks of input frames simultaneously. The method used for video compression is conventional block based motion vector estimation and a refined motion vector approximation that uses less side information for decoding. The implementation result shows that the proposed parallel method has better speedup than sequential algorithm and is very much suitable for real time applications like online video surveillance, video conferencing and telemedicine.