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

模糊矢量量化图像和视频压缩及其在低比特率通信中的应用

• 批准号: 18500169
• 负责人: YUKINORI Suzuki
• 金额: $ 2.57万
• 依托单位: Muroran Institute of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2006
• 资助国家: 日本
• 起止时间: 2006 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500169/
• 关键词: image compression; fractal dimension; variable block size; vector quantization; 動画像圧縮; 領域分割

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

矢量量化(VQ)在图像和视频压缩中得到了广泛的研究。我们正在关注用于图像和视频压缩的VQ。矢量量化需要较大的计算代价来构造码书(CB)。然而，一旦我们构造了CB，解码只涉及到从CB中搜索。因此，编码和解码图像的计算成本可以忽略不计。这对于图像压缩和应用都是非常有吸引力的。矢量量化由两部分组成：编码和解码。对于VQ的编码和解码，我们首先准备一个CB。构造CB是VQ压缩的关键，而构造方法是本课题的目的。在中，决定的图像的VQ压缩率和质量取决于CB中代码向量(CV)的大小。原则上，CB中的CV大小越大，解码图像的压缩比越低，而CB中的CV大小越小，决定的图像质量越高。这是压缩…之间的权衡更高的解码图像速率和质量。为了解决这一问题，提出了可变块大小的除法。以往的方法是基于四叉树(QT)分解来分割图像。QT分解是基于图像局部区域的同质性来进行的。然而，复杂区域可能具有财富信息，而同质区域可能没有。在这个意义上，我们使用局部分形维(LFD)实现了可变块大小的VQ。我们用感知图像质量度量(PIQM)来评价一幅解码图像。在这个项目中，我们提出了一种基于遗传算法的最优CB设计。遗传算法是一种寻找最优解的随机搜索方法。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。遗传算法在复杂的优化问题中得到了广泛的应用，并被证明能为这些问题提供良好的解决方案。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，这种能力对于构造用于图像压缩的VQ的最优CB也是有用的。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，而二进制编码的遗传算法不是。在实数编码的遗传算法中，基因是用实值而不是二进制值编码的。通过使用实数编码的遗传算法，由于不需要从基因型到表型的解码，因此可以用比二进制值表示的个体更小的尺寸来表示个体。这是实数编码遗传算法的一个优点。我们使用最小代沟(MGG)算法来选择个体。在MGG算法中，使用模拟的二进制交叉(SBX)来产生一个新的种群。因为图像是彩色图像，所以我们将其分为三个部分：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分成块。这些块是学习向量，有16384个向量，每个向量是16个维度。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连接在一起，为实数编码的GA生成一个个体。然后，产生30个个体作为种群。然后计算适应度函数。我们提出了一种变块大小的彩色图像矢量量化方法。利用局部分维进行图像分割。编码图像通过归一化感知图像质量度量(NPIQM)进行评价。实验结果表明，在块大小固定的情况下，压缩比与GB的压缩比几乎相同。然而，解码后的图像质量比固定块大小的CB解码的图像质量要好。NPIQM大于4.0，表示知觉水平4(良好)。较少

项目成果

検索順序符号化法に適合する色空間の実験的検討

兼容搜索顺序编码方法的色彩空间实验研究

• 发表时间: 2007
• 期刊: 電子情報通信学会技術報告 10
• 作者: Q. U. Khand;S. Dematapitiya;S. Saga;J. Maeda;Sumudu Dematapitiya;Kaoru Sato;笹崎和也;佐藤薫
• 通讯作者: 佐藤薫

画像の複雑さに基づく可変ブロックサイズによるべクトル量子化

基于图像复杂性的可变块大小的矢量量化

• 发表时间: 2007
• 期刊: 電子情報通信学会技術報告 10
• 作者: Q. U. Khand;S. Dematapitiya;S. Saga;J. Maeda;Sumudu Dematapitiya;Kaoru Sato;笹崎和也
• 通讯作者: 笹崎和也

Fuzzy vector quantization of images based on local fractal dimensions,

基于局部分形维数的图像模糊矢量量化，

• 发表时间: 2006
• 期刊: Fuzzy IEEE (in Press)
• 作者: T.Sasazaki;H.Ogasawara;S.Saga;J.Maeda;Y.Suzuki
• 通讯作者: Y.Suzuki

Experimental study on compatibility of search-order coding with color spaces

搜索顺序编码与色彩空间兼容性的实验研究

• 发表时间: 2007
• 期刊: Proceedings of 2007 IEEE Tree-Rivers Workshop on Soft Computing in Industrial Applications
• 作者: Q. U. Khand;S. Dematapitiya;S. Saga;J. Maeda;Sumudu Dematapitiya;Kaoru Sato
• 通讯作者: Kaoru Sato

Vector quantization of images with variable block size

• DOI: 10.1016/j.asoc.2007.05.002
• 发表时间: 2008-01-01
• 期刊: APPLIED SOFT COMPUTING
• 影响因子: 8.7
• 作者: Sasazaki, Kazuya;Saga, Sato;Suzuki, Yukinori
• 通讯作者: Suzuki, Yukinori