Parallel Linear Hashtable Motion Estimation Algorithm

This paper presents a parallel linear hashtable motion estimation algorithm (LHMEA). Most parallel video compression algorithms focus on group of picture (GOP). Based on LHMEA we proposed earl[1][2], we developed a parallel motion estimation algorithm focus inside of frame. We divide each reference...

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Vydáno v:2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing s. 277 - 282
Hlavní autoři: Yunsong Wu, Megson, G.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2006
Témata:
Clustering algorithms
Concurrent computing
Distributed computing
Encoding
High performance computing
Motion estimation
Systems engineering and theory
Transform coding
Vectors
Video compression
ISBN:9781424406562, 1424406560
ISSN:1551-2541
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Shrnutí:This paper presents a parallel linear hashtable motion estimation algorithm (LHMEA). Most parallel video compression algorithms focus on group of picture (GOP). Based on LHMEA we proposed earl[1][2], we developed a parallel motion estimation algorithm focus inside of frame. We divide each reference frames into equally sized regions. These regions are going to be processed in parallel to increase the encoding speed significantly. The theory and practice speed up of parallel LHMEA according to the number of PCs in the cluster are compared and discussed. Motion vectors (MV) are generated from the first-pass LHMEA and used as predictors for second-pass hexagonal search (HEXBS) motion estimation, which only searches a small number of Macroblocks (MBs). We evaluated distributed parallel implementation of LHMEA of TPA for real time video compression.
ISBN:9781424406562
1424406560
ISSN:1551-2541
DOI:10.1109/MLSP.2006.275561