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Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT)

Kim, Chanyul and O'Connor, Noel E. ORCID: 0000-0002-4033-9135 (2009) Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT). In: ISM 2009 - IEEE International Symposium on Multimedia, 14-16 December, 2009, San Diego, California, USA. ISBN 978-1-4244-5231-6

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Abstract

Variable Block Size (VBS) based motion estimation has been adapted in state of the art video coding, such as H.264/AVC, VC-1. However, a low complexity H.264/AVC encoder cannot take advantage of VBS due to its power consumption requirements. In this paper, we present a VBS partition algorithm based on a binary motion edge map without either initial motion estimation or Rate-Distortion (R-D) optimization for selecting modes. The proposed algorithm uses the Walsh Hadamard Transform (WHT) to create a binary edge map, which provides a computational complexity cost effectiveness compared to other light segmentation methods typically used to detect the required region.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Video compression
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Official URL:http://dx.doi.org/10.1109/ISM.2009.12
Copyright Information:©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Funders:Samsung Electronics, Science Foundation Ireland, SFI 07/CE/I1147
ID Code:14828
Deposited On:08 Sep 2009 14:17 by Hyowon Lee . Last Modified 09 Nov 2018 10:12

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