Mr. Rahul Khanna Profile

Rahul Khanna

at Intel Corp

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Proceedings Article | 18 January 2004 Paper

Reduced complexity genetic algorithm for motion estimation

Rahul Khanna, Mihaela van der Schaar

Proceedings Volume 5308, (2004) https://doi.org/10.1117/12.527750

KEYWORDS: Motion estimation, Genetic algorithms, Promethium, Genetics, Phase velocity, Detection and tracking algorithms, Video, Motion detection, Cameras, Optimization (mathematics)

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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.

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