| Recently, coming with the emergence of video applications under the network environment, the heterogeneous issue of Internet and the different display capabilities and computational power of terminal devices make video coding confront with the challenge in the wide dissemination and application of multimedia information. Therefore, the research target of video coding has been shifted from a pure compression viewpoint to designing a coding system that allows efficient transmission. The compressed stream should be capable of accommodating a variety of applications with diverse constraints in network bandwidth or receiver complexity. Scalable Video Coding (SVC) has come to widely attention because of the ability to solve these problems, and its core idea has been accepted by several international video standards.On the basis of the analysis of the wavelet-based scalable video coding framework and the scalable video coding framework based on the traditional hybrid coding structure, the work of this dissertation is concentrated on several key technologies involved in these two scalable video coding schemes: motion compensated temporal filtering (MCTF), motion estimation (ME) and error concealment (EC). Specifically, the main contributions of this dissertation are concluded as follows.Firstly, the development of video coding standard is presented, scalable video coding technology is described in detail, and the structure diagrams of encoding and decoding technology are given.Secondly, MCTF and the property of residual image based on MCTF are studied. The study of MCTF includes the following three aspects: 1) In order to achieve better filtering effect, the lifting process of MCTF with different length wavelet filter coefficients which make better use of relevant information between adjacent frames is studied; 2) According to the motion characteristics of the video sequence, an adaptive group of picture (GOP) structure is proposed; 3) To enhance time filtering flexibility, content adaptive update steps based on the property of the human vision system is presented. Based on these, an improved full-scalable video coding system is offered. In this system, the coded bit-stream is organized to achieve the brilliant combination of three main scalabilities: temporal, spatial and PSNR scalabilities. Experimental results show that the coding efficiency and the quality of reconstructed sequence are improved significantly. The study of the property of residual image based on MCTF in scalable video coding includes the energy non-stationary, temporal-spatial correlation and frequency property. The experiment results are given and analyzed. Thirdly, the high effective motion estimation algorithms and the effective evaluation criteria are studied through changing the mode of motion estimation and reducing the complexity of motion compensation. A new fast motion estimation algorithm based on moving direction prediction is presented, in which the high correlation of adjacently blocks'motion vectors and the center-biased characteristic of motion vectors in image sequences are used. The algorithm designs four kinds of patterns and then selects patterns to process the image according to its motion direction predicted by referenced motion vectors. High precision and high efficiency of the match and compensation can reduce prediction error and improve video compression effect, so the accuracy of block matching is the core issue. In this dissertation, a method of using the distribution of the image difference as a matching criterion is proposed, that is image difference variance matching criterion. Experimental results show that image difference variance matching criterion received relatively high codec quality.Finally, for the circumstance of losing image information caused by transmission error, three error concealment algorithms are proposed. The first one is an error concealment based on inter-frame information for video transmission. The missing blocks are classified into low activity blocks and high activity blocks by using the motion vector information of the surrounding correctly received blocks. The low activity blocks are concealed by the simple average motion vector (AVMV) method. For the high activity blocks, several closed convex sets are defined, and the method of projections onto convex sets (POCS) is used to recover the missing blocks by combining frequency and spatial domain information. The second one is an efficient spatio-temporal boundary matching algorithm (ESTBMA) which exploits both spatial and temporal information to reconstruct the lost motion vectors (MV) and also introduces a new side smoothness measurement. The motion vector corresponding to the minimum of the distortion function is used as the estimation of motion vector of the lost block. The third one is an error concealment algorithm based on the diffusion equation. A motion vectors estimated by ESTBMA is used to make a initial recovery for the lost information. Then, an anisotropy diffusion equation constructed by activity masking characteristics in human vision system (HVS) is used to make a refining recovery of lost information. The proposed algorithm can reduce the blocking artifacts of the recovered images, protect the structure information in the images, and obtain better visual effects.
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