After reviewing the digital video compression encoding approaches based on the source model employed, we point out that the object-oriented encoding at low bit rate is a very promising compression approach which is not only capable of overcoming the blocking artifacts produced in classical translational block-based methods but also of a object-based characteristic belonging to one of the important marks of the latest MPEG-4 standard.The object-oriented video compression algorithm has exceeded the traditional framework of source model theory. The basic notion behind the technique is to partition a image into several objects with arbitrary shape based on 3-D scene model. Each object is specified by three sets of parameters, namely, motion, shape as well as texture information. Among them, both motion estimation and shape encoding are particularly critical. In contrast to the traditional block based matching technique, the motion estimation in the object-oriented compression approach should be consistent with a representation of a scene in terms of objets. Furthermore, it should aim at reliably estimating the motion of the considered objects rather than providing a mere temporal prediction, and therefore more complex motion models should be used. The shape encoding should not only assure efficiently coding for objects boundary but also provide the advantage permitted manipulating objects easily in thereceiver. Therefore, both the motion estimation module and the shape encoding module are worth further investigation in the framework of the object-oriented motion images encoding. Furthermore, it should be noticed that the modules motioned above are both on the basis of the fast and efficient generation of objects in the scene. In this dissertation , we focus on the issues motioned above and several novel algorithms are presented.The achievements of the dissertation include:In this dissertation , a modified quadratic spatial transformation motion estimation is proposed. After performing a image intensity segmentation, in order to overcome the weaknesses of the original algorithm which converge tardily or even diverge in small region, the original algorithm is modified by forward-backward method as well as numerical approach to improve the accuracy and stability of the motion estimation.A fast temporal-spatio segmentation algorithm using quadratic spatial transformations is presented. Based on the image spatial segmentation and the estimation of the parameters of quadratic spatial transformations motion model, the amount of computation for temporal-spatio segmentation is efficiently decreased through the linearization the estimating for model parameters, householder transformation and the fast approach for R, Z matrix.A optimal contour graph encoding scheme based on both hexagonal neighborhood system and heap data structure is proposed. In this method, we employ maximum absolute distance as distortion measure and divide the contour graph into several contour chains onwhich the contour graph is optimally encoded. Since the single-source directed graph corresponding to the contour chain is sparse, we can efficiently reduce the time complexity in Dijkstra algorithm commonly uesd for the shortest-path problem by using a heap data structure. So that the vertex positions in piecewise linear approximation can be efficiently obtained. Furthermore, the region connectivity could not be lost because the optimization is targeted for each contour chain.In general, the encoding of the nodes is costly, therefore, in this dissertation we proposed a nodes encoding method which set up a tree dynamically in the encoder and decoder simultaneously in the process of deep-first traversing. Therefore, the cost for encoding nodes could be reduced efficiently because when any contour chain is decoded, its starting node could be chosen from the starting nodes of decoded contour chains in the receiver.A efficient encoding for piecewise linear approximation vertices based on hexagonal neighborhood system approach is proposed. Th