Dense Motion Fields for Digital Video Processing and Compression

This paper presents novel algorithms that perform motion estimation for video processing and compression. We observe that “smoothness” is a very important and intuitive property in the estimation of motion fields. It is pointed out that most current motion estimation techniques implement smoothness as a constraint, differing only in terms of the specific type of smoothness demanded from video data. This paper views smoothness as a property that is determined by the underlying video data rather than a predetermined and specific property that is imposed on video data. Instead of forcing the available video data to conform to an abstract smoothness model, we try to select the “smoothest” motion field that conforms to the available data. We propose fast and efficient techniques that determine a set of possible motion fields and that select the smoothest field from this set. Issues like quantization and embedded video compression (via embedded motion fields) are discussed.