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Computationally Efficient MCTF for MC-EZBC Scalable Video Coding Framework.

Authors :
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Pandu Rangan, C.
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Doug
Vardi, Moshe Y.
Weikum, Gerhard
Ghosh, Ashish
De, Rajat K.
Pal, Sankar K.
Karunakar, A. K.
Pai, M. M. Manohara
Source :
Pattern Recognition & Machine Intelligence (978-3-540-77045-9); 2007, p666-673, 8p
Publication Year :
2007

Abstract

The discrete wavelet transforms (DWTs) applied temporally under motion compensation (i.e. Motion Compensation Temporal Filtering (MCTF)) has recently become a very powerful tool in scalable video compression, especially when implemented through lifting. The major bottleneck for speed of the encoder is the computational complexity of the bidirectional motion estimation in MCTF. This paper proposes a novel predictive technique to reduce the computational complexity of MCTF. In the proposed technique the temporal filtering is done without motion compensation. The resultant high frequency frames are used to predict the blocks under motion. Motion estimation is carried out only for the predicted blocks under motion. This significantly reduces the number of blocks that undergoes motion estimation and hence the computationally complexity of MCTF is reduced by 44% to 92% over variety of standard test sequences without compromising the quality of the decoded video. The proposed algorithm is implemented in MC-EZBC, a 3D-subband scalable video coding system. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540770459
Database :
Complementary Index
Journal :
Pattern Recognition & Machine Intelligence (978-3-540-77045-9)
Publication Type :
Book
Accession number :
34135944
Full Text :
https://doi.org/10.1007/978-3-540-77046-6_82