This article is part of the series Design and Architectures for Signal and Image Processing 2009.

Open Access Research Article

FPSoC-Based Architecture for a Fast Motion Estimation Algorithm in H.264/AVC

Obianuju Ndili and Tokunbo Ogunfunmi*

Author Affiliations

Department of Electrical Engineering, Santa Clara University, Santa Clara, CA 95053, USA

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EURASIP Journal on Embedded Systems 2009, 2009:893897  doi:10.1155/2009/893897


The electronic version of this article is the complete one and can be found online at: http://jes.eurasipjournals.com/content/2009/1/893897


Received: 21 March 2009
Revisions received: 18 June 2009
Accepted: 27 October 2009
Published: 18 November 2009

© 2009 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

There is an increasing need for high quality video on low power, portable devices. Possible target applications range from entertainment and personal communications to security and health care. While H.264/AVC answers the need for high quality video at lower bit rates, it is significantly more complex than previous coding standards and thus results in greater power consumption in practical implementations. In particular, motion estimation (ME), in H.264/AVC consumes the largest power in an H.264/AVC encoder. It is therefore critical to speed-up integer ME in H.264/AVC via fast motion estimation (FME) algorithms and hardware acceleration. In this paper, we present our hardware oriented modifications to a hybrid FME algorithm, our architecture based on the modified algorithm, and our implementation and prototype on a PowerPC-based Field Programmable System on Chip (FPSoC). Our results show that the modified hybrid FME algorithm on average, outperforms previous state-of-the-art FME algorithms, while its losses when compared with FSME, in terms of PSNR performance and computation time, are insignificant. We show that although our implementation platform is FPGA-based, our implementation results compare favourably with previous architectures implemented on ASICs. Finally we also show an improvement over some existing architectures implemented on FPGAs.

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