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A novel gray image representation using overlapping rectangular NAM and extended shading approach

机译:使用重叠矩形NAM和扩展阴影方法的新型灰度图像表示

摘要

In this paper, inspired by the idea of overlapping rectangular region coding of binary images, we extend the SDS design, which is based on overlapping representation from binary images to gray images based on the non-symmetry and anti-packing model (NAM). A novel gray image representation is proposed by using the overlapping rectangular NAM (RNAM) and the extended Gouraud shading approach, which is called ORNAM representation. Also, we present an ORNAM representation algorithm of gray images. The encoding and the decoding of the proposed algorithm can be performed in O(n log n) time and O(n) time, respectively, where n denotes the number of pixels in a gray image. The wrong decoding problem of the hybrid matrix R for the overlapping RNAM representation of gray images is solved by using the horizontal, vertical, and isolated matrices, i.e., H, V and I, respectively, which are used to identify the vertex types. Also, we put forward four criteria of anti-packing homogeneous blocks. In addition, by redefining a codeword set for the three vertices symbols, we also propose a new coordinate data compression procedure for coding the coordinates of all non-zone elements in the three matrices H, V and I. By taking some idiomatic standard gray images in the field of image processing as typical test objects, and by comparing our proposed ORNAM representation with the conventional S-Tree Coding (STC) representation, the experimental results in this paper show that the former has higher compression ratio and less number of homogeneous blocks than the latter whereas maintaining a satisfactory image quality, and therefore it is a better method to represent gray images.
机译:在本文中,受二进制图像重叠矩形区域编码的想法的启发,我们扩展了SDS设计,该设计基于非对称和反打包模型(NAM),从二进制图像到灰度图像的重叠表示。通过使用重叠的矩形NAM(RNAM)和扩展的Gouraud阴影方法,提出了一种新颖的灰度图像表示,称为ORNAM表示。此外,我们提出了灰度图像的ORNAM表示算法。所提出算法的编码和解码可以分别在O(n log n)时间和O(n)时间中执行,其中n表示灰度图像中的像素数。通过使用水平,垂直和孤立矩阵(分别用于标识顶点类型)的水平,垂直和孤立矩阵,可以解决混合矩阵R对于灰色图像的重叠RNA表示的错误解码问题。此外,我们提出了抗堆积均质砖的四个标准。此外,通过重新定义三个顶点符号的代码字集,我们还提出了一种新的坐标数据压缩过程,用于对三个矩阵H,V和I中所有非区域元素的坐标进行编码。通过拍摄一些惯用的标准灰度图像在图像处理领域作为典型的测试对象,并且通过将我们提出的ORNAM表示与常规S-Tree编码(STC)表示进行比较,本文的实验结果表明,前者具有更高的压缩率和更少的同质块数量与后者相比,尽管保持了令人满意的图像质量,所以它是一种更好的表示灰度图像的方法。

著录项

  • 作者

    Zheng Y; Yu Z; You J; Sarem M;

  • 作者单位
  • 年度 2012
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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