Dept. of Electr. Eng., Sogang Univ., Seoul.
IEEE Trans Image Process. 1996;5(4):587-97. doi: 10.1109/83.491335.
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.
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 进行逼近,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地使用原始图像的灰度模式,而提出的分形编码方法则使用经过近似然后抽取的图像作为定义域池,并使用其灰度模式。因此,所提出的算法利用分形逼近而不受收缩映射的约束。对于原始图像的逼近,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,对于可变块大小分割,我们使用表示区域灰度表面粗糙度的块的分形维数。对几个测试图像的计算机模拟表明,对于静态图像的编码,所提出的方法比传统的分形编码方法具有更好的性能。