Prediction complexity-based HEVC parallel processing for asymmetric multicores

Prediction complexity-based HEVC parallel processing for asymmetric multicores This paper proposes a novel Tile allocation method considering the computational ability of asymmetric multicores as well as the computational complexity of each Tile. This paper measures the computational ability of asymmetric multicores in advance, and measures the computational complexity of each Tile by using the amount of HEVC prediction unit (PU) partitioning. The implemented system counts and sorts the amount of PU partitions of each Tile, and also allocates Tiles to asymmetric big.LITTLE cores according to their expected computational complexity. When experiments were conducted, the amount of PU partitioning and the computational complexity (decoding time) showed a close correlation, and average performance gains of decoding time with the proposed adaptive allocation were around 36 % with 12 Tiles, 28 % with 18 Tiles, and 31 % with 24 Tiles, respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Prediction complexity-based HEVC parallel processing for asymmetric multicores

Loading next page...
 
/lp/springer_journal/prediction-complexity-based-hevc-parallel-processing-for-asymmetric-PCVt92XKs2
Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4413-7
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a novel Tile allocation method considering the computational ability of asymmetric multicores as well as the computational complexity of each Tile. This paper measures the computational ability of asymmetric multicores in advance, and measures the computational complexity of each Tile by using the amount of HEVC prediction unit (PU) partitioning. The implemented system counts and sorts the amount of PU partitions of each Tile, and also allocates Tiles to asymmetric big.LITTLE cores according to their expected computational complexity. When experiments were conducted, the amount of PU partitioning and the computational complexity (decoding time) showed a close correlation, and average performance gains of decoding time with the proposed adaptive allocation were around 36 % with 12 Tiles, 28 % with 18 Tiles, and 31 % with 24 Tiles, respectively.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Feb 9, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off