Abstract
Memetic Algorithm by hybridization of Standard Particle Swarm Optimization and Global Local Best Particle Swarm Optimization is proposed in this paper. This technique is used to reduce number of computations of video compression by maintaining same or better quality of video. In the proposed technique, the position equation of Standard Particle Swarm Optimization is modified and used as step size equation to find best matching block in current frame. To achieve adaptive step size, time varying inertia weight is used instead of constant inertia weight for getting true motion vector dynamically. The time varying inertia weight is based up on previous motion vectors. The step size equation is used to predict best matching macro block in the reference frame with respect to macro block in the current frame for which motion vector is found. The result of proposed technique is compared with existing block matching algorithms. The performance of Memetic Algorithm is good as compared to existing algorithms in terms number of computations and accuracy.
Highlights
With the increasing popularity of technologies such as Internet streaming video and video conferencing, video compression has became an essential component of broadcast and entertainment media
The performance of Memetic Algorithm is compared with other existing methods such as exhaustive search (ES), SESTSS, Three Step Search (TSS), New Three Step Search (NTSS) and 4SS and the results are presented in Table 1 and Table 2
The performance of Memetic Algorithm is good as compared to existing algorithms except ES for video compression because it find best matching block with less computational cost by maintaining same accuracy of video
Summary
With the increasing popularity of technologies such as Internet streaming video and video conferencing, video compression has became an essential component of broadcast and entertainment media. Motion estimation has been popularly used in video signal processing, which is a fundamental component of video compression. Computational complexity varies from 70 percent to 90 percent for all video compression. To reduce the computational time of exhaustive search method, many other methods are proposed i.e. Simple and Efficient Search (SES)[1], Three Step Search (TSS)[2], New Three Step Search (NTSS)[2], Four step Search (4SS)[3], Diamond Search (DS)[4], Adaptive Road Pattern Search (ARPS)[5], Novel Cross Diamond search [6], New Cross-Diamond Search algorithm [7], Adaptive Block Matching Algorithm [8], Efficient Block Matching Motion Estimation [9], Content. Adaptive Video Compression [10] and Fast motion estimation algorithm [11]. We propose a Memetic Algorithm to reduce number of computations of video compression by maintaining same or better quality of video.
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