一种基于可重叠RNAM的灰度图像表示算法

Gray Image Representation Algorithm Based on Overlapping RNAM

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.

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 L 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 L 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.