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classified block
相关语句
  “classified block”译为未确定词的双语例句
     A Method of Image Classified Block Coding
     图象块编码—分类的方法
短句来源
     Considered the important characteristic anisotropism on spatial\|time frequency when the eye observe the image, and according to the Webb′s theorem, a filter to divide the images into three subimages is designed. The improved SOGM neural network MSOFM is employed for learning each classified block, and then encoding the image.
     并考虑图像的观察者———人眼的时 -空频率具有各向异性 ,根据韦伯定理设计一个滤波器 ,把图像块分成三个区域 ,运用改进的自适应矢量量化神经网络 (MSOFM )来训练各分类块并编制码书。
短句来源
  相似匹配句对
     Block and Z.
     B lock和Z.
短句来源
     A Method of Image Classified Block Coding
     图象块编码—分类的方法
短句来源
     Gypsum block
     石膏砌块
短句来源
     CLASSIFIED INDEX
     分类目录
短句来源
     A MULTI-LEVEL FRACTAL BLOCK CODINGBASED ON CLASSIFIED MATCHING
     多级分类分形块编码
短句来源
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Classified Block Coding has received more attention recently for the advantage of easy implementation. The classification and coding of blocks are the two important problems in classified block coding.In this paper, we propose a classified block coding algorithm based on DCT coding and polynomial approximation. In the algorithm, the original image is splitted into nonoverlapped 8×8 blocks. The blocks are classified into four classes: constant blocks, smooth blocks,...

Classified Block Coding has received more attention recently for the advantage of easy implementation. The classification and coding of blocks are the two important problems in classified block coding.In this paper, we propose a classified block coding algorithm based on DCT coding and polynomial approximation. In the algorithm, the original image is splitted into nonoverlapped 8×8 blocks. The blocks are classified into four classes: constant blocks, smooth blocks, coarse blocks and detail blocks, by using the intensity local variance, the polynomial approximation error and spatialfrequency distribution. The constant blocks and smooth blocks are approximated by 0order and 1order polynomial respectively. For the coarse and detail blocks, we compute and quantize their DCT coefficients. Then encode them by means of an improved runlength coding. The experiment results show that the proposed algorithm, without using entropy coder as postprocessor of the codes, has better performance than JPEG.

提出了一个基于DCT和二维多项式近似的块分类编码算法。在该算法中,原始图象被分割成互不覆盖的8×8子块。通过依次地利用灰度局部方差、二维多项式近似误差和图象信号的空间频率分布,把图象块分为均匀、平滑、粗糙和细节4类。均匀块和平滑块分别采用零阶和一阶多项式近似。粗糙和细节块先进行DCT变换,然后对其DCT系数量化后采用改进的游程编码表示。实验结果表明该算法具有良好的性能。在未采用熵编码为编码码流作后处理的情况下,性能仍优于JPEG标准。

In this paper, by using the modern models of human′s visual system and a series of the statistical features of natural images and the relative characteristics of the visual neuron, the image is divided into the diamond blocks, based on the characteristics of the vision physiology and psychology. Considered the important characteristic anisotropism on spatial\|time frequency when the eye observe the image, and according to the Webb′s theorem, a filter to divide the images into three subimages is designed. The...

In this paper, by using the modern models of human′s visual system and a series of the statistical features of natural images and the relative characteristics of the visual neuron, the image is divided into the diamond blocks, based on the characteristics of the vision physiology and psychology. Considered the important characteristic anisotropism on spatial\|time frequency when the eye observe the image, and according to the Webb′s theorem, a filter to divide the images into three subimages is designed. The improved SOGM neural network MSOFM is employed for learning each classified block, and then encoding the image. The experiment shows that the block effect of the reconstruction image is much improved.

文中根据视觉生理学和心理学特性 ,采用现有人类视觉系统模型及研究结果和一系列独立于自然图像统计特性及与人类视神经元相关的特性 ,把图像分成菱形块。并考虑图像的观察者———人眼的时 -空频率具有各向异性 ,根据韦伯定理设计一个滤波器 ,把图像块分成三个区域 ,运用改进的自适应矢量量化神经网络 (MSOFM )来训练各分类块并编制码书。计算机仿真结果表明 :重构图像的“块效应”明显得到改善。

Based on the principle of support vector machine,a classification model of expansive soil is established.Meanwhile,the classifying blocks of expansive soil are obtained through training a large set of expansive samples.Then,a prediction for the classification of residual expansive soils has been carried out using this model.The researching results show that the classification model of support vector machine presents excellent performance,high prediction accuracy and is easy to run.As a result,it is an...

Based on the principle of support vector machine,a classification model of expansive soil is established.Meanwhile,the classifying blocks of expansive soil are obtained through training a large set of expansive samples.Then,a prediction for the classification of residual expansive soils has been carried out using this model.The researching results show that the classification model of support vector machine presents excellent performance,high prediction accuracy and is easy to run.As a result,it is an effective way and has wide applying prospect.

将支持向量机方法应用于膨胀土分类问题中,建立了膨胀土分类的支持向量机模型。以膨胀土实测数据为学习样本,经过训练,得到膨胀土的分类区间。应用该模型对剩余的膨胀土数据进行预测,预测结果表明支持向量机分类模型性能良好、预测精度高、简便易行,是膨胀土判别的一种有效方法,具有广阔的应用前景。

 
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