Reduced complexity genetic algorithm for motion estimation

Rahul Khanna; Mihaela van der Schaar

doi:10.1117/12.527750

18 January 2004 Reduced complexity genetic algorithm for motion estimation

Rahul Khanna, Mihaela van der Schaar

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Proceedings Volume 5308, Visual Communications and Image Processing 2004; (2004) https://doi.org/10.1117/12.527750
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

In order to achieve high video coding efficiency, a new motion estimation and compensation algorithm is proposed based on Genetic Algorithm. This algorithm exploits the uniformity and correlation in the properties of the cluster of blocks called Super-Block. These Super-Blocks have adaptive boundaries that are used to partially generate initial population for fast convergence to global minimum. Rest of the population is generated using pure Random Number Generator (RNG). This population then generates offspring which then competes within itself by the virtue of it’s fitness to survive into the next generation. The fitness value in each generation is calculated by comparing the reference frame with the predicted frame. The algorithm stops after convergence or when maximum generations are reached. This algorithm compares well against conventional algorithms like FSA (Full Search Algorithm), One-Step Method or N-Step Method in terms of number of searches, complexity, robustness and scalability.

Citation Download Citation

Rahul Khanna and Mihaela van der Schaar "Reduced complexity genetic algorithm for motion estimation", Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); https://doi.org/10.1117/12.527750

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KEYWORDS

Motion estimation

Genetic algorithms

Promethium

Genetics

Phase velocity

Detection and tracking algorithms

Cameras

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