A fractal vector quantizer for image coding
A fractal vector quantizer for image coding
复制标题
用于图像编码的分形矢量量化器
DOI：
10.1109/83.725366
复制
发表时间：
1998
期刊：
影响因子：
--
通讯作者：
Sang Uk Lee
中科院分区：
文献类型：
--
作者：
Chang;R. Kim;Sang Uk Lee
研究了矢量量化与分形图像编码技术的关系，提出了一种新的基于分形矢量量化的静止图像编码算法。在FVQ中，源图像被固定的基块粗略地逼近，码本是从粗略逼近的图像而不是从外部训练集或源图像本身来自训练的。因此，FVQ除了能够有效地利用真实图像的自相似性外，还能够在没有任何边信息的情况下消除码本中的冗余。计算机仿真结果表明，该算法在峰值信噪比(PSNR)方面优于其他大多数基于分形的编码器。
We investigate the relation between VQ (vector quantization) and fractal image coding techniques, and propose a novel algorithm for still image coding, based on fractal vector quantization (FVQ). In FVQ, the source image is approximated coarsely by fixed basis blocks, and the codebook is self-trained from the coarsely approximated image, rather than from an outside training set or the source image itself. Therefore, FVQ is capable of eliminating the redundancy in the codebook without any side information, in addition to exploiting the self-similarity in real images effectively. The computer simulation results demonstrate that the proposed algorithm provides better peak signal-to-noise ratio (PSNR) performance than most other fractal-based coders.