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Still image coding based on vector quantization and fractal approximation

机译:基于矢量量化和分形逼近的静止图像编码

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摘要

In this paper, we propose a coding algorithm for still images using vector quantization (VQ) and fractal approximation, in which low-frequency components of an input image are approximated by VQ, and its residual is coded by fractal mapping. The conventional fractal coding algorithms indirectly used the gray patterns of an original image with contraction mapping, whereas the proposed fractal coding method employs an approximated and then decimated image as a domain pool and uses its gray patterns. Thus, the proposed algorithm utilizes fractal approximation without the constraint of contraction mapping. For approximation of an original image, we employ the discrete cosine transform (DCT) rather than conventional polynomial-based transforms. In addition, for variable blocksize segmentation, we use the fractal dimension of a block that represents the roughness of the gray surface of a region. Computer simulations with several test images show that the proposed method shows better performance than the conventional fractal coding methods for encoding still pictures.
机译:在本文中,我们提出了一种使用矢量量化(VQ)和分形逼近的静态图像编码算法,其中输入图像的低频分量通过VQ逼近,其残差通过分形映射进行编码。传统的分形编码算法通过收缩映射间接使用原始图像的灰度模式,而提出的分形编码方法将近似然后抽取的图像用作域池,并使用其灰度模式。因此,所提出的算法利用了分形逼近,而没有收缩映射的约束。为了近似原始图像,我们采用离散余弦变换(DCT),而不是传统的基于多项式的变换。此外,对于可变块分割,我们使用块的分形维数来表示区域灰色表面的粗糙度。计算机模拟了几张测试图像,结果表明,与传统的分形编码方法相比,该方法具有更好的性能。

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