Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Resolution Genetic Algorithm and Its Application in Motion Estimation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A Fast Adaptive Statistical Genetic Motion Search Algorithm for H.264/AVC^1
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 01
Novel Point-Oriented Inner Searches for Fast Block Motion Estimation
IEEE Transactions on Multimedia
A lightweight genetic block-matching algorithm for video coding
IEEE Transactions on Circuits and Systems for Video Technology
Hexagon-based search pattern for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
On the Design of Pattern-Based Block Motion Estimation Algorithms
IEEE Transactions on Circuits and Systems for Video Technology
Genetic motion search algorithm for video compression
IEEE Transactions on Circuits and Systems for Video Technology
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Genetic algorithm-based motion estimation schemes play a significant role in improving the results of H.264/AVC standardization efforts when addressing conversational and non-conversational video applications. In this paper, we present a robust motion estimation scheme that uses a noble genetic trail bounded approximation (GTBA) approach to speed up the encoding process of H.264/AVC video compression and to reduce the number of bits required to code frame. The proposed algorithm is utilized to enhance the fitness function strength by integrating trail information of motion vector and sum of absolute difference (SAD) information into a fitness function. Experimental results reveal that the proposed GTBA resolves conflict obstacles with respect to both the number of bits required to code frames and the execution time for estimation.