Improved Update Step for Scalable Video Coding in Video Surveillance

Abstract

Many evolving video services and applications for intelligent security systems require reliable transmission of high quality video to diverse clients over heterogeneous networks using available system resources. Scalable video coding (SVC) is one of the emerging video compression technologies with such potential capabilities. Advances in lifting-based motion-compensated temporal filtering (MCTF) have enabled highly efficient and flexible spatial, temporal, signal-to-noise ratio (SNR), and complexity scalability to be realized over a wide range of bit rates. In this paper, we present an algorithm to improve the update step of MCTF, which serves as an important informative step for the coding performance of SVC. A novel update-step algorithm, which takes advantage of the chrominance information of the video sequence and the correlation of the motion vectors (MVs) of the neighboring blocks as well as the correlation of the derived update MVs in the low-pass frames, is proposed to improve update step of MCTF by (1) computing correct update motion information, (2) generating correct amount of energy contained in the high-pass frames. Experimental results show that the proposed algorithm can significantly improve the quality of the reconstructed video sequence in visual quality.