An Adaptive Search Algorithm Based on Block Classification for Fast Block Motion Estimation

Abstract

This paper presents a new motion estimation algorithm, called the adaptive motion estimation (AME). The AME algorithm exploits the information gathered from the previous frame to derive a parameter, called CF (correlation parameter), and employs CF to classify the blocks in the current frame into potentially dependent blocks and potentially independent blocks. AME applies different motion estimation methods for potentially dependent blocks and potentially independent blocks to achieve better estimation accuracy and lower computational complexity. Simulation results showed that the proposed AME algorithm has both lower computational complexity and higher PSNR than other motion estimation algorithms, such as three-step search (TSS), new three-step search (NTSS), four-step search (4SS), and the NPSA (new predictive search area) algorithm.