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Reconfigurable SAD tree architecture based on adaptive sub-sampling in HDTV application
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Great Lakes Symposium on VLSI archive
Proceedings of the 19th ACM Great Lakes symposium on VLSI table of contents
Boston Area, MA, USA
SESSION: VLSI design table of contents
Pages 463-468  
Year of Publication: 2009
ISBN:978-1-60558-522-2
Authors
Yiqing Huang  Waseda University, Fukuoka, Japan
Qin Liu  Waseda University, Fukuoka, Japan
Satoshi Goto  Waseda University, Fukuoka, Japan
Takeshi Ikenaga  Waseda University, Fukuoka, Japan
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

In H.264/AVC based integer motion estimation engine, fixed architectures based on full pixel or direct sub-sampling pattern are widely used for HDTV application. However, these architectures suffer from either high complexity or quality loss problems. In this paper, an adaptive sub-sampling based reconfigurable architecture is given out. Firstly, by executing pixel difference analysis, the adaptive sub-sampling scheme which uses three hardware friendly patterns is applied on homogeneous macroblock (MB). Secondly, the related architecture introduces one more pipeline stage to build up configurable partial SAD values so that system performance is enhanced. Thirdly, a two-level pixel data organization scheme is proposed to solve data reuse and hardware utilization problems caused by adaptive algorithm. Moreover, one cross based SAD generation structure is introduced to achieve adaptive output results with less hardware cost. Experimental results show that, the proposed architecture can averagely save 61.71% clock cycles and accomplish twice or four times processing capability for homogeneous MBs. The maximum clock frequency is 208MHz under the TSMC 0.18um technology in worst case conditions(1.62V, 125 C).


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Yiqing Huang: colleagues
Qin Liu: colleagues
Satoshi Goto: colleagues
Takeshi Ikenaga: colleagues