Hardware-software collaborative complexity reduction scheme for the emerging HEVC intra encoder

Authors:
Muhammad Usman Karim Khan;Muhammad Shafique;Mateus Grellert;Jörg Henkel
Affiliations:
Karlsruhe Institute of Technology (KIT), Germany;Karlsruhe Institute of Technology (KIT), Germany;Karlsruhe Institute of Technology (KIT), Germany;Karlsruhe Institute of Technology (KIT), Germany
Venue:
Proceedings of the Conference on Design, Automation and Test in Europe
Year:
2013

Citing 4
Cited 1

Optimizing the H.264/AVC Video Encoder Application Structure for Reconfigurable and Application-Specific Platforms

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An HVS-based adaptive computational complexity reduction scheme for H.264/AVC video encoder using prognostic early mode exclusion

Proceedings of the Conference on Design, Automation and Test in Europe
Improved intra mode signaling for HEVC

ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Adaptive power management of on-chip video memory for multiview video coding

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AMBER: adaptive energy management for on-chip hybrid video memories

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Abstract

High Efficiency Video Coding (HEVC/H.265) is an emerging standard for video compression that provides almost double compression efficiency at the cost of major computational complexity increase as compared to current industry-standard Advanced Video Coding (AVC/H.264). This work proposes a collaborative hardware and software scheme for complexity reduction in an HEVC Intra encoding system, with run-time adaptivity. Our scheme leverages video content properties which drive the complexity management layer (software) to generate a highly probable coding configuration. The intra prediction size and direction are estimated for the prediction unit which provides reduced computational-complexity. At the hardware layer, specialized coprocessors with enhanced reusability are employed as accelerators. Additionally, depending upon the video properties, the software layer administers the energy management of the hardware coprocessors. Experimental results show that a complexity reduction of up to 60% and the energy reduction up to 42% are achieved.