Motion estimation and compensation is the key to high quality video coding. Block matching motion estimation is used in most video codecs, including MPEG-2, MPEG-4, H.263 and H.26L. Motion estimation is also a key component in the digital restoration of archived video and for post-production and special effects in the movie industry. Sub-pixel accurate motion vectors can improve the quality of the vector field and lead to more efficient video coding. However sub-pixel accuracy requires interpolation of the image data. Image interpolation is a key requirement of many image processing algorithms. Often interpolation can be a bottleneck in these
applications, especially in motion estimation due to the large number pixels involved. In this paper we propose using commodity computer graphics hardware for fast image interpolation. We use the full search block matching algorithm to illustrate the problems and limitations of using graphics hardware in this way.
Motion estimation and compensation is a key component in video procesing. Motion estimation is necessary for high quality compression. It is also a key component in archive video restoration and motion picture post-production. Very accurate motion vectors are usually required in the latter two applications. More accurate motion vectors can also lead to greater coding efficiency. Real-time, accurate motion estimation is currently not attainable on standard desktop PCs. It usually requires some kind of dedicated hardware such as on video coding chips. Gradient based motion estimation is one which gives good accuracy for reasonable computational cost. This paper uses the Wiener based motion estimator as a vehicle to explore the acceleration of gradient based motion estimation on the PC.