The Open UniversitySkip to content
 

A new efficient similarity metric and generic computation strategy for pattern-based very low bit-rate video coding

Manoranjan, P.; Murshed, M. and Dooley, L. S. (2004). A new efficient similarity metric and generic computation strategy for pattern-based very low bit-rate video coding. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’04), 17-21 May 2004, Montreal.

Full text available as:
[img]
Preview
PDF (Not Set) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (243Kb)
DOI (Digital Object Identifier) Link: http://doi.org/10.1109/ICASSP.2004.1326507
Google Scholar: Look up in Google Scholar

Abstract

In the context of very low bit-rate video coding, pattern representations of a moving region (MR) in block-based motion estimation and compensation has become increasingly attractive. Generally, all existing pattern-matching algorithms apply a similarity metric, involving elementary operations, to compute the mismatch between an MR and a particular fixed pattern in order to select the best-matching pattern from a fixed-size codebook of predefined patterns. An efficient similarity metric, together with a new generic computation strategy, is presented by considering only the mismatch areas of MRs. It is theoretically proven that for a specific MR in a macroblock, the new similarity metric selects exactly the same pattern as existing metrics, while the resulting computational coding efficiency is improved by between 21% and 58% compared with the H.263 low bit-rate coding standard.

Item Type: Conference Item
ISSN: 1520-6149
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 11425
Depositing User: Laurence Dooley
Date Deposited: 20 Aug 2008 06:07
Last Modified: 23 Feb 2016 17:30
URI: http://oro.open.ac.uk/id/eprint/11425
Share this page:

Altmetrics

Scopus Citations

► Automated document suggestions from open access sources

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk