A Frequency Based Hierarchical Fast Search Block Matching Algorithm for Fast Video Communication

Numerous fast-search block motion estimation algorithms have been developed to circumvent the high computational cost required by the full-search algorithm. These techniques however often converge to a local minimum, which makes them subject to noise and matching errors. Hence, many spatial domain b...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 7; no. 4
Main Authors: Al-Najdawi, Nijad, Tedmori, Sara, A., Omar, Dorgham, Osama, A., Jafar
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2016
Subjects:
Algorithms
Complexity
Matching
Motion simulation
Search algorithms
Video communication
ISSN:2158-107X, 2156-5570
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Numerous fast-search block motion estimation algorithms have been developed to circumvent the high computational cost required by the full-search algorithm. These techniques however often converge to a local minimum, which makes them subject to noise and matching errors. Hence, many spatial domain block matching algorithms have been developed in literature. These algorithms exploit the high correlation that exists between pixels inside each frame block. However, with the block transformed frequencies, block matching can be used to test the similarities between a subset of selected frequencies that correctly identify each block uniquely; therefore fewer comparisons are performed resulting in a considerable reduction in complexity. In this work, a two-level hierarchical fast search motion estimation algorithm is proposed in the frequency domain. This algorithm incorporates a novel search pattern at the top level of the hierarchy. The proposed hierarchical method for motion estimation not only produces consistent motion vectors within each large object, but also accurately estimates the motion of small objects with a substantial reduction in complexity when compared to other benchmark algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2016.070459