Abstract
In this paper, we describe a motion prediction technique for video compression applications. The proposed technique utilizes statistical characterization of difference pictures of a video source, which can be described a Laplacian distribution reflecting both temporal and spatial correlation of the consecutive image frames. The prediction of unkown motion vectors in a short time-step ahead is formulated mathematically as a minimization of a global objective function which gives the solution to the unknown functionals, (u(x,y,t),v(x,y,t)), at each individual pixel location. This minimization is equivalent to solving stochastic coupled-elliptical partial differential equations under a natural boundary condition. From the physical condition, regularization process is employed and the stochastic partial differential equations are converted to a stochastic linear algebraic system AX =b with a sparse matrix A. Then an iteretive algorithm based on successive over-relaxation technique is utilized to predict the motion-vector field. The unique features of this technique include the utilization of difference picture for mathematical formulation which allows a better statistical characterization of video sources, and the computation of the mot ion-vector field based on the prediction.
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Li, H.H., Sun, S., Panturu, D. (1997). Motion Vector Prediction Based on Frame Differences. In: Li, H.H., Sun, S., Derin, H. (eds) Video Data Compression for Multimedia Computing. The Springer International Series in Engineering and Computer Science, vol 378. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6239-9_11
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DOI: https://doi.org/10.1007/978-1-4615-6239-9_11
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