Devolopment of Mean and Median Based Adaptive Search Algorithm for Motion Estimation in SNR Scalable Video Coding

Abstract

Now a day’s quality of video in encoding is challenging in many video applications like video conferences, live streaming and video surveillance. The development of technology has resulted in invention of various devices, different network conditions and many more. This has made video coding challenging day by day. An answer to the need of all can be scalable video coding, where a single bit stream contains more than one layer known as base and enhancement layers respectively. There are various types of scalability as spatial, SNR, temporal scalability. Among these three types of scalability, SNR scalability deals with the quality of the frames i.e. base layers includes least quality frames and enhancement layer gets frames with better quality. Motion estimation is the most important aspect of video coding. Usually the adjacent frames of a video are very much similar to each other. Hence to increase the coding efficiency to remove redundancy as well as to reduce computational complexity,motion should be estimatedand compensated.Hence, in the scalable video coding, videos have been encoded in SNR scalability mode and then the motion estimation has been carried out by two proposed methods.The approach depends on eliminating the unnecessary blocks, which have not undergone motion, by taking the specific threshold value for every search region. It is desirable to reduce the time of computation to increase the efficiency but keeping in view that not at the cost of much quality. In second method, the search method has been optimized using ‘particle swarm optimization’ (PSO) technique, which is a method of computation aims at optimizing a problem with the help of popular candidate solutions.In block matching based on PSO, a swarm of particles will fly in random directions in search window of reference frame, which can be indexed by the horizontal and vertical coordinates of the center pixel of the candidate block. These algorithm mainly used to reducing the computational time by checking some random position points in the search window for finding out the best match.PSO algorithm estimate the motion with very low complexity in the context of video estimation. Both the methods have been analyzed and performance have been compared with various video sequences.The proposed technique out performs to the existing techniques in terms of computational complexity and video quality