一种基于可重叠RNAM 的灰度图像表示算法

借助于二值图像的可重叠矩形区域编码的思想,通过使用可重叠矩形非对称逆布局的模式表示模型(RNAM)和扩展的Gouraud阴影法,给出了可重叠同类块逆布局的4个准则,提出了一种基于可重叠RNAM的灰度图像表示算法,简称为ORNAMC表示算法.在ORNAMC表示算法中,通过使用3个用于标识顶点类型的水平矩阵H、垂直矩阵V和单点矩阵I代替混合矩阵R,解决了灰度图像的可重叠RNAM表示中矩阵R的不可解码性问题;同时,通过将顶点类型及码字进行重新定义,提出了一种对矩阵H,V和I中所有非零元素坐标进行编码的坐标数据压缩算法.以图像处理领域里惯用的标准灰度图像等作为典型测试对象,实验结果表明,与已提出的非重叠RNAMC和流行的STC,SDCT等灰度图像表示方法相比,在保持图像质量的前提下,ORNAMC表示方法具有更高的压缩比和更少的块数,因而是灰度图像表示的一种更好的方法.

Gray Image Representation Algorithm Based on Overlapping RNAM

The idea of an overlapping rectangular region coding of binary images inspired the overlapping rectangular non-symmetry, the anti-packing pattern representation model (RNAM), and the extended Gouraud shading approach. A novel lossy gray image representation algorithm based on the overlapping RNAM, which is called ORNAMC representation algorithm, is proposed. Also, the four principles for anti-packing the overlapping homogenous blocks are presented in this paper. In the proposed ORNAMC representation algorithm, the wrong decoding problem of the matrix R for the overlapping RNAM representation of gray images is solved separately by using the horizontal, vertical, and isolated matrices, i.e.,H, V and I. These are used to identify the vertex types instead of using a single hybrid matrix, i.e., R. In addition, by redefining the codeword set for the three vertices symbols, this paper proposed a new coordinate data compression algorithm 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 the proposed ORNAMC representation algorithm with the latest non-overlapping RNAMC, the experimental results show that the former has a higher compression ratio and a fewer number of blocks and yet, maintains image quality. Therefore, a better method is to represent the gray image.