Semi-hierarchical based motion estimation algorithm for the dirac video encoder

Tun, Myo, Loo, Jonathan and Cosmas, John (2008) Semi-hierarchical based motion estimation algorithm for the dirac video encoder. WSEAS Transactions on signal Processing, 4 (5) . pp. 261-270. ISSN 1790-5052 [Article]

[img]
Preview
PDF - Published version (with publisher's formatting)
Download (607kB) | Preview

Abstract

Having fast and efficient motion estimation is crucial in today’s advance video compression technique since it determines the compression efficiency and the complexity of a video encoder. In this paper, a method which we call semi-hierarchical motion estimation is proposed for the Dirac video encoder. By considering the fully hierarchical motion estimation only for a certain type of inter frame encoding, complexity of the motion estimation can be greatly reduced while maintaining the desirable accuracy. The experimental results show that the proposed algorithm gives two to three times reduction in terms of the number of SAD calculation compared with existing motion estimation algorithm of Dirac for the same motion estimation accuracy, compression efficiency and PSNR performance. Moreover, depending upon the complexity of the test sequence, the proposed algorithm has the ability to increase or decrease the search range in order to maintain the accuracy of the motion estimation to a certain level.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science > SensoLab group
A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 7819
Notes on copyright: We acknowledge full permission from WSEAS to include published version. (5/11)
Useful Links:
Depositing User: Jonathan Loo
Date Deposited: 28 Apr 2011 15:54
Last Modified: 30 Nov 2022 01:43
URI: https://eprints.mdx.ac.uk/id/eprint/7819

Actions (login required)

View Item View Item

Statistics

Activity Overview
6 month trend
113Downloads
6 month trend
398Hits

Additional statistics are available via IRStats2.