Kim, Chanyul and O'Connor, Noel E. ORCID: 0000-0002-4033-9135 (2009) Fast intra prediction in the transform domain. In: DCC 2009 - Data Compression Conference, 16-18 March 2009, Snowbird, Utah, USA. ISBN 978-1-4244-3753-5
Abstract
In this paper, we present a fast intra prediction method based on separating the transformed coefficients. The
prediction block can be obtained from the transformed and quantized neighboring block generating minimum distortion
for each DC and AC coefficients independently. Two prediction methods are proposed, one is full block search
prediction (FBSP) and the other is edge based distance prediction (EBDP), that find the best matched transformed
coefficients on additional neighboring blocks. Experimental results show that the use of transform coefficients
greatly enhances the efficiency of intra prediction whilst keeping complexity low compared to H.264/AVC.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Subjects: | Computer Science > Video compression Computer Science > Image processing Computer Science > Algorithms Computer Science > Digital video |
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 |
Publisher: | Institute of Electrical and Electronics Engineers |
Official URL: | http://dx.doi.org/10.1109/DCC.2009.26 |
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: | Science Foundation Ireland, Samsung Electronics Ltd |
ID Code: | 3634 |
Deposited On: | 26 Mar 2009 13:31 by Hyowon Lee . Last Modified 09 Nov 2018 10:09 |
Documents
Full text available as:
Preview |
PDF
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
465kB |
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record