Parallel array architectures for motion estimation

Motion estimation is one of the most computationally intensive tasks required in digital video compression. The authors propose parallelizable motion estimation algorithms with low computational cost for both sub-optimal and optimal motion estimation. For efficient optimal motion estimation, they develop theoretical bounds based on convexity to reduce the required operations. All algorithms are tested on multiple motion estimation paradigms for performance evaluation