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首页> 外文期刊>IEEE Transactions on Circuits and Systems for Video Technology >VLSI Implementation of an Adaptive Edge-Enhanced Image Scalar for Real-Time Multimedia Applications
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VLSI Implementation of an Adaptive Edge-Enhanced Image Scalar for Real-Time Multimedia Applications

机译:实时多媒体应用的自适应边缘增强图像标量的VLSI实现

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摘要

In this paper, a low-complexity adaptive edge-enhanced algorithm is proposed for the implementation of 2-D image scaling applications. The proposed novel algorithm consists of a linear space-variant edge detector, a low complexity sharpening spatial filter, and a simplified bilinear interpolation. The edge detector is designed to discover the image edges by a low-cost edge-catching technique. The sharpening spatial filter is added as a prefilter to reduce the blurring effect produced by the bilinear interpolation. Furthermore, an adaptive technology is used to enhance the effect of the edge detector by adaptively selecting the input pixels of the bilinear interpolation. In addition, an algebraic manipulation and a hardware sharing techniques are used to simplify bilinear interpolation, which efficiently reduces the computing resources and silicon area in very large scale integration (VLSI) circuits. By adding eight 8-bit registers as a register bank, this design can process streaming data directly and requires only a one-line-buffer memory. The VLSI architecture of this paper contains 6.67-K gate counts and achieves about 280-MHz processing rate by using the TSMC 0.13-um CMOS process. Compared with previous low-complexity techniques, this paper performs with better quality, higher performance, less memory requirements, and lower hardware cost than other image scaling methods.
机译:在本文中,为实现二维图像缩放应用,提出了一种低复杂度的自适应边缘增强算法。提出的新算法包括线性空间变量边缘检测器,低复杂度锐化空间滤波器和简化的双线性插值。边缘检测器旨在通过低成本的边缘捕捉技术发现图像边缘。添加了锐化空间滤波器作为预滤波器,以减少由双线性插值产生的模糊效果。此外,通过自适应选择双线性插值的输入像素,使用自适应技术来增强边缘检测器的效果。另外,使用代数运算和硬件共享技术来简化双线性插值,从而有效减少超大规模集成电路(VLSI)电路中的计算资源和硅面积。通过添加八个8位寄存器作为寄存器组,该设计可以直接处理流数据,并且仅需要一个行缓冲存储器。本文的VLSI架构包含6.67-K的门数,并且通过使用TSMC 0.13um CMOS工艺实现了约280MHz的处理速率。与以前的低复杂度技术相比,本文比其他图像缩放方法具有更高的质量,更高的性能,更少的内存需求以及更低的硬件成本。

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