Paper Info
Title
Linear hashtable method predicted hexagonal search algorithm with spatial related criterion
Abstract
The paper presents a novel Linear Hashtable Method Predicted Hexagonal Search (LHMPHS) method for block base motion compensation. It bases on the edge motion estimation algorithm called hexagonal search (HEXBS). Most current variances of hexagonal search are investigated. On the basis of research of previous algorithms, we proposed a Linear Hashtable Motion Estimation Algorithm (LHMEA). The proposed algorithm introduces hashtable into motion estimation. It uses information from the current frame. The criterion uses spatially correlated macroblock (MB)'s information. Except for coarse search, the spatially correlated information is also used in inner search. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms such as Full Search, Logarithmic Search etc. The evaluation considers the three important metrics: time, compression rate and PSNR.
Year
DOI
Venue
2005
10.1007/11499145_122
SCIA
Keywords
Field
DocType
full search,coarse search,inner search,current algorithm,predicted hexagonal search,current frame,hexagonal search algorithm,spatial related criterion,block base motion compensation,current variance,edge motion estimation algorithm,hexagonal search,linear hashtable method,motion estimation,search algorithm,spatial relation,spatial correlation
Macroblock,Data compression ratio,Search algorithm,Pattern recognition,Computer science,Motion compensation,Image processing,Artificial intelligence,Binary search algorithm,Motion estimation,Motion vector
Conference
Volume
ISSN
ISBN
3540
0302-9743
3-540-26320-9
Citations 
PageRank 
References 
0
0.34
11
Authors
4
Name
Order
Citations
PageRank
Yunsong Wu163.07
Graham Megson2134.03
Zhengang Nie311.05
F. N. Alavi4132.76