GPU-Based Sample-Parallel Context Modeling for EBCOT in JPEG2000

MATELA, Jiří, Vít RUSŇÁK and Petr HOLUB. GPU-Based Sample-Parallel Context Modeling for EBCOT in JPEG2000. In Sixth Doctoral Workshop on Mathematical and Engineering Methods in Computer Science -- Selected Papers. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2011, p. 77--84. ISBN 978-3-939897-22-4.

Basic information

• Original name: GPU-Based Sample-Parallel Context Modeling for EBCOT in JPEG2000
• Authors: MATELA, Jiří (203 Czech Republic, belonging to the institution), Vít RUSŇÁK (203 Czech Republic, belonging to the institution) and Petr HOLUB (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Dagstuhl, Germany, Sixth Doctoral Workshop on Mathematical and Engineering Methods in Computer Science -- Selected Papers, p. 77--84, 8 pp. 2011.
• Publisher: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Germany
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: printed version "print"
• WWW: URL
• RIV identification code: RIV/00216224:14330/11:00049746
• Organization unit: Faculty of Informatics
• ISBN: 978-3-939897-22-4
• ISSN: 2190-6807
• Keywords in English: EBCOT;JPEG2000;Tier-1;GPU;context modeller
• Tags: International impact, Reviewed

Abstract

Embedded Block Coding with Optimal Truncation (EBCOT) is the fundamental and computationally very demanding part of the compression process of JPEG2000 image compression standard. EBCOT itself consists of two tiers. In Tier-1, image samples are compressed using context modeling and arithmetic coding. Resulting bit-stream is further formated and truncated in Tier-2. JPEG2000 has a number of applications in various fields where the processing speed and/or latency is a crucial attribute and the main limitation with state of the art implementations. In this paper we propose a new parallel approach to EBCOT context modeling that truly exploits massively parallel capabilities of modern GPUs and enables concurrent processing of individual image samples. Performance evaluation of our prototype shows speedup 12 times for the context modeller, and 1.4--5.3 times for the whole EBCOT Tier-1, which includes not yet optimized arithmetic coder.

Links

• GD102/09/H042, research and development project: Name: Matematické a inženýrské metody pro vývoj spolehlivých a bezpečných paralelních a distribuovaných počítačových systémů

Investor: Czech Science Foundation

• MSM0021622419, plan (intention): Name: Vysoce paralelní a distribuované výpočetní systémy

Investor: Ministry of Education, Youth and Sports of the CR, Highly Parallel and Distributed Computing Systems

• MUNI/A/0914/2009, interní kód MU: Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace (Acronym: SV-FI MAV)

Investor: Masaryk University, Category A