Generalized partial distortion search algorithm for block-matching motion estimation

The quality against speed control for real-time video applications, such as the low-bit-rate video conferencing or the high quality video entertainment, usually absents from many traditional fast block motion estimators. In this paper, a novel block-matching algorithm for fast motion estimation named generalized partial distortion search algorithm (GPDS) is proposed. It uses halfway-stop technique with progressive partial distortion (PPD) to increase the chance of early rejection of impossible candidate motion vectors at very early stages. Simulations on PPD show that 28 to 38 times computational reduction with only 0.45-0.50dB PSNR performance degradation as compared to full search algorithm. In addition, a new normalized partial distortion comparison method is also proposed for enabling the control of searching speed against the prediction accuracy by a quality factor k. This method also generalizes the conventional partial distortion search algorithm when k is equal to 1, and the normalized partial distortion search algorithm (NPDS) when k is equal to infinity. Experimental results show that GPDS with use of PPD could provide PSNR performance very close to full search algorithm with 7 to 17 times, and to NPDS with 22 to 33 times speedup, respectively, as compared to full search algorithm.