Artificially Intelligent Adaptive Search Fast Motion Estimation Algorithm for HD Video

This paper presents a new Machine Learning based approach to video Fast Motion Estimation, which improves quality, minimizes power consumption and provides control over the performance vs power balance, rendering it very suitable for hardware implementation into a motion co-processor. Many mobile and hand-held devices today deploy such hardware accelerators. The main goal of the presented algorithm is to achieve maximum quality Motion Estimation per unit of consumed power by minimizing the number of search points and also providing an optional mechanism for finding an optimal early termination point. The paper presents the creation of a dictionary of adaptively pre-learned fixed search patterns along with a pre-trained neural network to adaptively help select the most adequate search pattern from a dictionary according to the dynamics of the motion within a specific region of the video frame, not the frame or scene as a whole. There are often motions in various directions within the same scene or frame and the ability to focus on a local region within the frame improves quality significantly. Full Search represents the quality goal and upper boundary for any integer Fast Motion Estimation. The presented algorithm adds about 1 dB of PSNR to state-of-the-art fixed search patterns. There is only about 0.5 dB of PSNR remaining between our algorithm and Full Search.