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CLC number: TN79+1; TP752

On-line Access: 2011-09-09

Received: 2011-02-18

Revision Accepted: 2011-06-30

Crosschecked: 2011-08-22

Cited: 4

Clicked: 7755

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.9 P.743-753


Efficient implementation of a cubic-convolution based image scaling engine

Author(s):  Xiang Wang, Yong Ding, Ming-yu Liu, Xiao-lang Yan

Affiliation(s):  Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   wangxiang@vlsi.zju.edu.cn, dingy@vlsi.zju.edu.cn

Key Words:  Cubic-convolution, Hardware implementation, Interpolation, Engine

Xiang Wang, Yong Ding, Ming-yu Liu, Xiao-lang Yan. Efficient implementation of a cubic-convolution based image scaling engine[J]. Journal of Zhejiang University Science C, 2011, 12(9): 743-753.

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journal="Journal of Zhejiang University Science C",
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%T Efficient implementation of a cubic-convolution based image scaling engine
%A Xiang Wang
%A Yong Ding
%A Ming-yu Liu
%A Xiao-lang Yan
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100040

T1 - Efficient implementation of a cubic-convolution based image scaling engine
A1 - Xiang Wang
A1 - Yong Ding
A1 - Ming-yu Liu
A1 - Xiao-lang Yan
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 9
SP - 743
EP - 753
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100040

In video applications, real-time image scaling techniques are often required. In this paper, an efficient implementation of a scaling engine based on 4×4 cubic convolution is proposed. The cubic convolution has a better performance than other traditional interpolation kernels and can also be realized on hardware. The engine is designed to perform arbitrary scaling ratios with an image resolution smaller than 2560×1920 pixels and can scale up or down, in horizontal or vertical direction. It is composed of four functional units and five line buffers, which makes it more competitive than conventional architectures. A strict fixed-point strategy is applied to minimize the quantization errors of hardware realization. Experimental results show that the engine provides a better image quality and a comparatively lower hardware cost than reference implementations.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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