Image and Video compression with Fuzzy Vector Quantization and Application to Low bits Rate Communication

矢量量化（VQ）在图像和视频压缩中得到了广泛的研究。我们正在注意用于图像和视频压缩的VQ。矢量量化需要大量的计算成本来构造一个码本（CB）。然而，一旦我们构造了CB，解码只涉及从CB搜索。因此，编码和解码图像的计算成本是可以忽略的。这对于图像压缩和应用是非常有吸引力的。矢量量化由编码和解码两部分组成。为了使用VQ进行编码和解码，我们首先准备CB。矢量量化压缩的关键是构造CB，而构造CB的方法是本课题的研究目的。在中，决定图像的VQ压缩率和质量取决于CB中的码向量（CV）的大小。原则上，CB中的CV的尺寸越大，解码图像的压缩率越低，而CB中的CV的尺寸越小，判定图像的质量越高。这是一个压缩之间的权衡 ...更多信息 解码图像的速率和质量。为了解决这个问题，提出了可变块大小的划分。以往的图像分割方法都是基于四叉树（QT）分解，QT分解是基于图像局部区域的均匀性。然而，复杂区域可能具有丰富的信息，而均匀区域可能不具有。在这个意义上，我们实现了VQ与可变块大小使用局部分形维数（LFD）。我们通过感知图像质量测量（PIQM）来评估解码图像。在这个计画中，我们提出了一个使用遗传演算法的最佳化电路板设计。GA是一种随机搜索方法，用于寻找最优解。遗传算法的思想是基于自然选择和遗传学的机制。遗传算法的基本步骤包括选择、交叉和变异。气体已被广泛用于复杂的优化问题，并已被证明为这些问题提供了很好的解决方案。遗传算法的一个优点是它能够在多维空间中找到全局最优解，这种能力也有助于构造图像压缩的矢量量化的最优CB。我们使用实数编码的遗传算法来设计CB。它的变量空间是连续的，而二进制编码的GA不是。在实值编码遗传算法中，基因是由真实的值而不是二进制值编码的。通过使用实数编码的GA，因为它是不必要的解码从基因型到表型，个人可以表示在更短的大小比那些表示的二进制值。这是实数编码遗传算法的一个优点。我们使用最小代沟（MGG）算法的选择的个人。在MGG算法中，模拟二进制交叉SBX）被用来产生一个新的人口。由于图像是彩色图像，因此我们将其分为三个分量：红色、绿色和蓝色。CB是为各个颜色分量构建的。每个颜色分量的图像被划分为块。这些块是学习向量，有16384个向量，每个向量是16维。我们使用GA从这些向量生成255个CV。计算CV的算法如下所示。首先，从16384个向量中随机选择255个CV作为初始CV。这255个CV被连接以生成用于实数编码GA的一个个体。然后，生成30个个体作为种群。然后计算适应度函数。我们提出了一种用于彩色图像的可变块大小的矢量量化方法。利用局部分形维数进行图像分割。编码后的图像采用归一化感知图像质量测量（NPIQM）进行评价。实验结果表明，该算法的压缩率与固定块大小的GB的压缩率基本相同。然而，解码图像质量是上级的CB与固定块大小的解码。NPIQM大于4.0，意味着感知水平4（良好）。少

Vector quantization (VQ) has been widely studied for image and video compression. We are taking notice of VQ for image and video compression. VQ requires the large computational cost to construct a code book (CB). However, once we construct a CB, decoding involves only search form the CB. Therefore, the computational cost to encode and decode an image is negligible. This is very attractive point for image compression and applications. VQ consists of two parts: encoding and decoding. For encoding and decoding with VQ we first prepare a CB. To construct a CB is essential for VQ compression and the method to construct is purpose of this project. In, VQ compression rate and quality of the decided image depends on the size of code vectors (CVs) in a CB. In principle, the larger the size of CVs in a CB is, the lower is the compression rate of the decode image, while the smaller the size of CVs in a CB is, the higher is the quality of the decided image. This is a trade-off between compression … More rate and quality of a decoded image. To solve this problem, division with variable block size was proposed. The previous methods divide an image based on quad-tree (QT) decomposition A QT decompose image based on homogeneity of local regions of an image. However, the complex regions may have wealth information, but homogeneous region may not. In this sense, we implemented VQ with variable block size using local fractal dimensions (LFDs). We evaluated a decoded image by perceptual image quality measure (PIQM). In this project, we proposed an optimal CB design suing a genetic algorithm (GA). A GA is stochastic search method for finding optimal solution. The idea of a GA is based on the mechanism of natural selection and genetics. The basic procedure of a GA consists of selection, a crossover, and mutation. Gas have been widely used in complex optimization problems and have been shown to provide good solutions for these problems. An advantageous point of a GA is its ability to find a global optimal solution in multidimensional space, and this ability is also useful for constructing an optimal CB of VQ for image compression. We used the real-coded GA to design a CB. Its variable space is continuous, while the binary-coded GA is not. In the real-coded GA, genes are coded by real values instead of binary values. By using real-coded GA, since it is not necessary to decode from genotype to phenotype, individuals can be represented in shorter size than those represented by binary value. This ian advantageous point of the real-coded GA. We use the minimal generation gap (MGG) algorithm for the selection of individuals. In the MGG algorithm, simulated binary crossover 'SBX) is employed to generate a ne population. Since an image is a color image, we divide in into three components: red, green, and blue. The CB is constructed for individual color components. The image of each color component is divided into blocks. The blocks are learning vectors and there are 16384 vector, each of which is 16 dimensions. We generate 255 CVs from these vectors using GA. The algorithm to compute CVs is as follows. First, 255 CVs are chosen out of 16384 vectors as the initial CVs randomly. These 255 CVs are connected to generate one individual for the real-coded GA. Then, 30 individuals are generated as population. A fitness function is then computed. We propose vector quantization with variable block size for color images. Image division was carried out using local fractal dimension. Encoded image was evaluated by normalized perceptual image quality measure (NPIQM). Compression rate was also evaluated by bit per pixel Results of experiments show that compression rate is almost the same as that in the case of a GB with fixed block size. However decoded image quality is superior to that decoded by the CB with fixed block size. NPIQM is larger than 4.0, meaning perceptual level 4 (good). Less