A Novel Based Approach for Finding Motion Estimation in Video Compression

Abstract: This paper mainly focuses on the two main classification of motion estimation algorithms used for video compression. Motion Estimation (ME) algorithms vary with respect to the a priori information and constraints they employ, as well as the method of computation they use to obtain the estimate. The classifications for ME algorithms are based on feature/region matching, gradient based methods, spatiotemporal energy methods, deterministic model based methods and stochastic model based methods. This paper focuses more on block matching algorithms which comes under feature/region matching and gradient based methods. This paper compares 2 important ME algorithms. They are Full Search (FS) and a newly found adaptive algorithm called AMEA. In FS every candidate points will be evaluated and more time will be taken for predicting the suitable motion vectors. Based on the above noted drawback, the above said adaptive algorithm called AMEA is proposed. These algorithms are used for implementing various standards, which ranges from MPEG1 / H.261 to MPEG4 / H.263 and H.264/AVC. A very brief introduction to the entire flow of video compression is also presented in this paper.

Keywords: AMEA, block matching, gradient based method, motion estimation, MPEG, H.261, H.263, H.264/AVC, video compression.