Constructing a general image codebook using vector quantization and application to mobile communication terminals.

电信网络正在全球范围内蔓延。人们可以使用互联网进行不受空间限制的交流。在这种情况下，对传输带宽和存储空间的需求继续超过现有技术的能力。因此，图像和视频压缩技术对于有效利用通信网络至关重要。在本研究项目中，我们开发了一种新的基于矢量量化的图像压缩方法。基于局部分维(LFD)来确定用于准备码本的训练向量的大小。在这个意义上，划分学习图像的每个块大小由LFD确定。原则上，训练向量中的像素数越小，码本(低比特率)就越大。此外，码本中的像素数越小，编码图像的质量越高。这是压缩速率和编码图像质量之间的折衷。我们假设这种权衡可以通过可变块大小的方法来解决。使用判别分析方法来执行划分图像区域的块大小。LFD值的范围是2.0到3.0。我们将LFD的直方图分成F个簇。然后我们准备了子码本，每个子码本对应于单个码矢量的大小。通过这种划分，我们构造了一个码本来对图像进行编码，并利用广义Lloyd算法(GLA)从训练向量中计算出码本向量。从压缩比和编码图像质量两个方面对该算法的性能进行了评估，并与使用其他算法构造码本的算法进行了比较。实验表明，该算法较好地解决了压缩比和图像质量之间的折衷问题。新开发的算法可用于移动通信终端的图像通信。

Telecommunication networks are spreading worldwide. People can communicate with each other beyond spatial restrictions using the Internet. In this situation, the demand for transmission bandwidth and storage space continues to outstrip the capacity of existing technologies. Image and video compression technology is therefore essential for the effective use of communication networks. In this research project, we have developed a new method to compress images using vector quantization. The sizes of the training vectors to prepare a codebook are determined on the basis of local fractal dimensions (LFDs). In this sense, each block size to divide the learning image is determined by the LFDs. In principle, the smaller the number of pixels in a training vector is, the larger is a codebook (low bit rate). Furthermore, the smaller the number of pixels in a codebook is, the higher is the quality of the encoded image. This is a tradeoff between compression rate and quality of the encoded image. We assume that this tradeoff can be solved by a method of variable block sizes. The block size to divide an area of an image is carried out using a method of discriminant analysis. The range of LFD values is 2.0 to 3.0. We divide the histogram of LFDs into F clusters. We then prepared sub-codebooks each of which corresponds to individual code-vector size. By this division, we construct a codebook to encode images.Furthermore, the code-vectors are computed from the training vectors using a generalized Lloyd algorithm (GLA). The performance of the proposed algorithm was evaluated by compression rate and quality of encoded images in comparison with those using other algorithms to construct a codebook. The experiments showed that the proposed algorithm solved the tradeoff between compression rate and quality of image. The newly developed algorithm could be useful for image communication for mobile communication terminals.