Motion compensation based on spatial transformations

Given the small bit allocation for motion information in very low bit-rate coding, motion compensation using the block matching (BMA) algorithm fails to maintain an acceptable level of prediction errors. The reason is that the motion model, or spatial transformation, assumed in block matching cannot approximate the motion in the real world precisely with a small number of parameters (i.e. motion vectors). To develop an effective motion compensation method for very low bit-rate video coding, we address the issues of 1) adopting more sophisticated spatial transformations than block matching, and 2) developing a motion estimation algorithm that is suitable for these spatial transformations. The spatial transformations discussed here are affine transformation, bilinear transformation and perspective transformation. Two new motion estimation algorithms, a matching-based algorithm and its fast version, are presented. The performance of the motion compensation methods, which combine the spatial transformations and the proposed motion estimation algorithms, is evaluated theoretically and experimentally using the following criteria: prediction error, amount of motion information, and computational cost. The simulation results show that in the proposed method, the amount of motion information required to achieve a fixed level of prediction error is reduced to one fourth that of block matching. We combine the designed motion compensation method with discrete cosine transform to simulate coding of CIF format images at the transmission rate of 16 kb/s. The quality of the decoded images in this method is substantially improved compared with the conventional H.261-based methods.