Paper
8 March 2005 Efficient mapping of the H.264 encoding algorithm onto multiprocessor DSPs
Amit Gulati, George Campbell
Author Affiliations +
Proceedings Volume 5683, Embedded Processors for Multimedia and Communications II; (2005) https://doi.org/10.1117/12.593980
Event: Electronic Imaging 2005, 2005, San Jose, California, United States
Abstract
With the introduction of a variety of novel coding tools in H.264 has come an increase in complexity that few processor architectures can facilitate. Prior coding loops, such as MPEG-2, provided fewer variations and optional capabilities as a part of the standard implementation; and as such they were readily partitioned in an intuitive manner with little deviation. Induced by the need to scale to such high-complexity algorithms, homogenous multiprocessor architectures are becoming more common. H.264 poses with it several new options to the software architect in approaching the issue of partitioning the coding blocks most efficiently across a multiprocessor architecture. In this paper, we address issues that arise from the mapping of H.264 onto Multiprocessor DSP chips. We discuss aspects of algorithm partitioning, reference frame coherency, and synchronization issues. We show flexible methods for mapping the algorithm onto MDSPs which allow scalability over coding tools, resolutions, and computation/bandwidth availability.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amit Gulati and George Campbell "Efficient mapping of the H.264 encoding algorithm onto multiprocessor DSPs", Proc. SPIE 5683, Embedded Processors for Multimedia and Communications II, (8 March 2005); https://doi.org/10.1117/12.593980
Lens.org Logo
CITATIONS
Cited by 12 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Digital signal processing

Computer programming

Video

Video processing

Computer architecture

Video compression

Data processing

RELATED CONTENT


Back to Top