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Image filtering in the block DCT domain using symmetric convolution

机译:使用对称卷积在块DCT域中进行图像滤波

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摘要

Processing of images in the transform domain saves computation by avoiding inverse and re-transform operations. In this paper, we present a technique for filtering of images in the transform domain using symmetric convolution in the block DCT space. Due to the application of convolution-multiplication property in the DCT domain, the filtering operation requires significantly less computation than its equivalent in the original signal/image space. To take care of discontinuities along boundaries of blocks, filtering is performed on a larger DCT block composed from adjacent blocks. Subsequently, the filtered DCT block is obtained by decomposing it. The proposed filtering technique achieves the same results of linear convolution in the spatial domain with reduced cost. With the proposed filtering, it is possible to significantly speedup the operation by ignoring some elements in the filtering matrices whose magnitudes are smaller than a threshold value. Typical sparseness of DCT domain input blocks is also considered for further reduction of computational cost. The proposed method uses simple linear operations such as matrix multiplication, which is appropriate for efficient hardware implementations. We also demonstrate its applications in image sharpening and removal of blocking artifacts directly in the compressed domain.
机译:通过避免反变换和重新变换操作,在变换域中处理图像可以节省计算量。在本文中,我们提出了一种在块DCT空间中使用对称卷积对变换域中的图像进行滤波的技术。由于在DCT域中应用了卷积乘法属性,因此与原始信号/图像空间中的等效滤波操作相比,滤波操作所需的计算量要少得多。为了照顾沿着块边界的不连续性,对由相邻块组成的更大的DCT块执行滤波。随后,通过分解获得滤波后的DCT块。所提出的滤波技术以降低的成本实现了在空间域中线性卷积的相同结果。利用所提出的滤波,可以通过忽略滤波矩阵中幅度小于阈值的某些元素来显着加速操作。还考虑了DCT域输入块的典型稀疏性,以进一步降低计算成本。所提出的方法使用简单的线性运算,例如矩阵乘法,适用于有效的硬件实现。我们还演示了其在图像锐化和直接在压缩域中消除块状伪像的应用。

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