Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

A fast full search algorithm for variable mock-based motion estimation of H.264

Authors
Lim, C.Kang, H.S.Kim, Tae YongYoo, K.Y.
Issue Date
Dec-2005
Publisher
SPRINGER-VERLAG BERLIN
Citation
ADVANCES IN VISUAL COMPUTING, PROCEEDINGS, v.3804, pp 710 - 717
Pages
8
Journal Title
ADVANCES IN VISUAL COMPUTING, PROCEEDINGS
Volume
3804
Start Page
710
End Page
717
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52841
DOI
10.1007/11595755_90
ISSN
0302-9743
1611-3349
Abstract
In this paper, we propose a novel fast motion estimation algorithm based on successive elimination algorithm (SEA) which can dramatically reduce complexity of the variable block size motion estimation in H.264 encoder. The proposed method applies the conventional SEA in the hierarchical manner to the seven block modes. That is, the proposed algorithm can remove the unnecessary computation of SAD by means of the process that the previous minimum SAD is compared to a current bound value which is obtained by accumulating current sum norms and reused SAD of 4x4 blocks for the bigger block sizes than 4x4. As a result, we have tighter bound in the inequality between SAD and sum norm than the bound in the ordinary SEA. If the basic size of the block is smaller than 4x4, the bound will become tighter but it also causes to increase computational complexity, especially addition operations for sum norm. Compared with fast full search algorithm of JM of H.264, our algorithm saves 60 to 70% of computation on average for several image sequences.
Files in This Item
Go to Link
Appears in
Collections
Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kim, Tae Yong photo

Kim, Tae Yong
첨단영상대학원 (영상학과)
Read more

Altmetrics

Total Views & Downloads

BROWSE