Motion Estimation for Content Adaptive Video Compression

Motion estimation is a multistep process that encompasses techniques such as motion vector prediction, determination of search range and search patterns, and identification of termination criteria. Each of these techniques has several diversions that may suit a particular set of video characteristics. It would be hard to conceive a universal algorithm that can perform well for all kinds of video contents. However, if important characteristics of a video sequence can be identified and utilized for adjusting various steps of motion estimation, one can design an adjustable algorithm that can tune its parameters to suit the video at hand. A multistage motion estimation algorithm that includes a pre-stage for analyzing the motion characteristics of a video sequence online is proposed. This stage predicts the motion vector field (MVF) from the previous coded frame, and clusters macroblocks into background and foreground regions based on the predicted MVF. The information from the pre-stage are passed on to the next stage, which includes a mathematical model for block distortion surface to estimate the distance from the current search point to the global optimal position. This allows the motion estimation stage to adjust its search strategies accordingly. The search is performed on a precise area according to the statistical properties of the motion vector prediction error, separately for foreground and background regions. The proposed algorithm, including the pre-stage, is fast and, therefore, is suitable for online and real-time encoding. Extensive simulation results obtained for several video sequences affirm the effectiveness of the proposed algorithm. The self-tuning property enables the algorithm to perform well for several types of benchmark sequences, yielding better video quality without exceeding the time complexity as compared to the other predictive motion estimation algorithms.

I. Introduction

Avideo encoder includes a number of components, out of which motion estimation (ME) carries a greater significance because it can consume more than 80% of the overall processing power and also because it has a high impact on video quality. The goal of an ME algorithm is to find the most accurate motion vectors (MVs) with the minimum processing cost. Reducing complexity, such as limiting the number of search positions, degrades visual quality. Therefore, ME is an optimization process of finding the best trade-off between visual quality and complexity. Since it is hard to conceive a single ME algorithm that can optimally perform for all video sequences, the question arises whether enough intelligence can be incorporated in an algorithm such that it can tune itself for best quality, accuracy, speed, or a combination of these objectives.