Performance analysis of block matching motion estimation algorithms for HD videos with different search parameters

High Definition (HD) videos are the most widely used in HD television and mobile phones now a days for transmission and storage. Due to large data size, HD videos require efficient and robust video coding mechanism to enable real-time encoding. Numerous Motion Estimation (ME) algorithms are proposed to reduce the computational complexity of the coding process. In this paper, we present the performance analysis of some famous Block Matching ME Algorithms (BMAs) for HD videos. Different performance measuring parameters are used to evaluate the performance of BMAs, like Peak Signal to Noise Ratio (PSNR), ME time, Mean Square Error (MSE). The simulation results show that the Adaptive Rood Pattern Search (ARPS) ME algorithm outperforms in term of MSE, PSNR and number of search points, for HD (720p) videos, over various search parameters. ARPS, Diamond Search (DS) and Flatted Hexagon Search (FHS) ME algorithms improve the PSNR from 32dB to 48dB for some video sequences, by increasing search range, whereas the number of search points also increased with the same parameter that causes to increased ME time and computational complexity.