Run-time adaptive energy-aware motion and disparity estimation in multiview video coding

Bruno Zatt, Muhammad Shafique, Felipe Sampaio, Luciano Agostini, Sergio Bampi, Jörg Henkel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel run-time adaptive energy-aware Motion and Disparity Estimation (ME, DE) architecture for Multiview Video Coding (MVC). It incorporates efficient memory access and data prefetching techniques for jointly reducing the on/off-chip memory energy consumption. A dynamically expanding search window is constructed at run time to reduce the off-chip memory accesses. Considering the multi-stage processing nature of advanced fast ME/DE schemes, a reduced-sized multi-bank on-chip memory is employed which can be power-gated depending upon the video properties. As a result, when tested for various video sequence, our approach provides a dynamic energy reduction of 82-96% for the off-chip memory and a leakage energy reduction of 57-75% for the on-chip memory compared to the Level-C and Level-C+ [7] prefetching techniques (which are the prominent data reuse and prefetching techniques in ME for video coding). The proposed ME/DE architecture is synthesized using a 65nm IBM low power technology. Compared to state-of-the-art MVC ME/DE hardware [14], our architecture provides 66% and 72% reduction in the area and power consumption, respectively. Moreover, our scheme achieves 30fps ME/DE 4-view HD1080p encoding with a power consumption of 74mW.

Original languageEnglish (US)
Title of host publication2011 48th ACM/EDAC/IEEE Design Automation Conference, DAC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1026-1031
Number of pages6
ISBN (Print)9781450306362
DOIs
StatePublished - 2011

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Keywords

  • Energy-Aware Design
  • Motion and Disparity Estimation
  • MVC
  • On-Chip Memory
  • Video Coding

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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