Parallel Adaptive Temporal Prediction with Load Balancing For Fast Video 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 adaptive motion estimation model for video compression using cluster computing on a local network with balanced load. The method used for temporal prediction is adaptive, in the manner in which, frames with very few motion changes are predicted in its integer wavelet domain and for high motion activity frame, motion compensation is applied in its spatial domain. This approach gives good compression rate. Secondly we apply a parallel compression model by having a multiple networked heterogeneous personal computer systems that perform compression on different input frames simultaneously. Also computing load is distributed properly among all processors by resource management technique of cluster computing. The implementation result shows that the proposed parallel method has better speedup than sequential algorithm and very much suitable for online video applications.