Distributed Video Coding Side-information Research

With the rapid development of 3-Generation communication, a lot of low power video devices appear. Due to the limit storage and battery power, these devices require low-complexity encoding, which poses challenge to traditional video coding algorithms.Recently, distributed video coding (DVC) is a new video encoding, due to the calculation of large correlation algorithm of motion estimation move from the encoder to the decoder to achieve a low-complexity encoding algorithm, this feature makes it reality for wireless video communications. Thus it has been the research hotspot in the field of video coding. Traditional video coding owes one success to the use of motion estimation algorithm in encoder, the existing motion estimation DVC framework will be transferred to the decoding end, which using the decoded recovery frame in the decoder to estimate the motion to generate the side information. However, the inaccurate recovery frame will cause the inaccurate motion estimation, this will result in degradation of the performance of side information and the DVC ultimately. Therefore, in order to improve the rate distortion performance of DVC, the paper will examine how to improve the performance of motion estimation in order to obtain more accurate side information.First,the article based on DVC theory introduces several studies of the traditional motion estimation methods, focusing on discuss block matching motion estimation based on several fast searching algorithm complexity and rate distortion performance. Second, it researches several of the existing methods for generating the side information, focusing on the classical motion compensation interpolation in time domain method of generating side information and the latest expectation maximization (EM) algorithm based on the maximum a posteriori probability, and do Experimental Comparison of the performance of each method, As can be seen, the side information that EM algorithm generates obtains the highest rate-distortion performance. As EM algorithm learning motion vectors needs a long time in this basis, the technical program combines the proposed diamond search to the EM algorithm, and experimental results show that it can shorten the learning time under maintaining a high rate distortion performance of the case. By studying the algorithm, we find that the initial probability model plays a key role in the motion vectors probability learning process. Therefore, the adaptive probability-based EM algorithm is presented. Experimental results show that compared with previous EM algorithm, the algorithm we presented reduces the learning time under maintaining the same rate-distortion performance of the case.