Share Email Print

Proceedings Paper

Reduced complexity genetic algorithm for motion estimation
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 18 January 2004
PDF: 12 pages
Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); doi: 10.1117/12.527750
Show Author Affiliations
Rahul Khanna, Intel Corp. (United States)
Mihaela van der Schaar, Univ. of California/Davis (United States)

Published in SPIE Proceedings Vol. 5308:
Visual Communications and Image Processing 2004
Sethuraman Panchanathan; Bhaskaran Vasudev, Editor(s)

© SPIE. Terms of Use
Back to Top