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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.

This paper proposes new methods for analyzing image sequences and updating textures of the three-dimensional (3-D) facial model. The paper also describes a method for synthesizing various facial expressions. These three methods are the key technologies for the model-based image coding system. The input image analysis technique directly and robustly estimates the 3-D head motions and the facial expressions without two-dimensional (2-D) any entity correspondences. This technique resolves the 2-D correspondence mismatch errors and provides quality reproduction of the original images by fully incorporating the synthesis rules. To verify analysis algorithm, the paper performs quantitative and subjective evaluations. The paper presents two methods for updating the texture of the facial model to improve the quality of the synthesized images. The first method focuses on the facial parts with large change of the brightness according to the various facial expressions for saving of the transmission bit rates. On the other hand, the second method focuses on all changes of brightness caused by the 3-D head motions as well as the facial expressions. The transmission bit rates are estimated according to the update methods. For synthesizing the output images, the paper describes rules that simulate the facial muscular actions because the muscles cause the facial expressions. These rules more easily synthesize the high-quality facial images that represent the various facial expressions. Now, the 34 rules for the muscular actions have been developed.