Computationally efficient MCTF for MC-EZBC scalable video coding framework

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence - Second International Conference, PReMI 2007, Proceedings
Pages666-673
Number of pages8
Volume4815 LNCS
Publication statusPublished - 2007
Event2nd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2007 - Kolkata, India
Duration: 18-12-200722-12-2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4815 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2007
CountryIndia
CityKolkata
Period18-12-0722-12-07

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Karunakar, A. K., & Pai, M. M. M. (2007). Computationally efficient MCTF for MC-EZBC scalable video coding framework. In Pattern Recognition and Machine Intelligence - Second International Conference, PReMI 2007, Proceedings (Vol. 4815 LNCS, pp. 666-673). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4815 LNCS).