Video Compression by Memetic Algorithm
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2011, Vol 2, Issue 6
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.
Authors and Affiliations
Pooja Nagpal , Seema Baghla
Keywords
Related Articles
- EP ID EP139912
- DOI -
- Views 154
- Downloads 0
Processing Sampled Big Data
Big data processing requires extremely powerful and large computing setup. This puts bottleneck not only on processing infrastructure but also many researchers don’t get the freedom to analyze large datasets. This paper...
Fingerprint Gender Classification using Univariate Decision Tree (J48)
Data mining is the process of analyzing data from a different category. This data provide information and data mining will extracts a new knowledge from it and a new useful information is created. Decision tree learning...
A Hybrid Framework using RBF and SVM for Direct Marketing
One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using...
Communication-Load Impact on the Performance of Processor Allocation Strategies in 2-D Mesh Multicomputer Systems
A number of processor allocation strategies have been proposed in literature. A key performance factor that can highlight the difference between these strategies is the amount of communication conducted between the paral...
Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods
Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of l...