JOURNAL ARTICLE

Hardware Implementation for the HEVC Fractional Motion Estimation Targeting Real-Time and Low-Energy

Abstract

This paper presents an energy-aware and high-throughput hardware design for the Fractional Motion Estimation (FME) compliant with the High Efficiency Video Coding (HEVC) standard. An extensive software evaluation was performed to guide the hardware design. The adopted strategy mainly consists in using only the four squareshaped Prediction Unit (PU) sizes rather than using all 24 possible PU sizes in the Motion Estimation (ME). This approach reduces about 59% the total encoding time and, as a penalty, it leads to an increase of only 4% in the bit rate for the same image quality. Together with this simplification, a multiplierless approach, algebraic optimizations and low-power techniques were applied to the hardware design to reduce the hardware-resource usage and the energy consumption, maintaining a high processing rate. The architecture was described in VHDL and the synthesis results for ASIC 45nm Nangate standard cells demonstrate that the developed architecture is able to process Ultra-High Definition (UHD) 2160p videos at 60 frames per second (fps), with the lowest power consumption and the lowest hardware-resource usage among the related works.

Keywords:
Computer science Motion estimation Hardware architecture Frame rate Coding (social sciences) VHDL Software Computer hardware Energy consumption Application-specific integrated circuit Power consumption Efficient energy use Embedded system Field-programmable gate array Power (physics) Algorithm Artificial intelligence Mathematics

Metrics

27
Cited By
2.51
FWCI (Field Weighted Citation Impact)
14
Refs
0.91
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Video Coding and Compression Technologies
Physical Sciences →  Computer Science →  Signal Processing
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Data Compression Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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