Yunping Zheng

A novel algorithm for triangle non-symmetry and anti-packing pattern representation model of gray images

The triangle packing problem has yielded many significant theories and applications such as textile cutting and container stuffing. Although the representation method of the popular linear quadtree has many merits, it puts too much emphasis upon the symmetry of image segmentation. Therefore, it is not the optimal representation method. In this paper, inspired by the concept of the triangle packing problem, we present a Triangle Nonsymmetry and Anti-packing pattern representation Model (TNAM). Also, we propose a novel algorithm for the TNAM of the gray images. By comparing the algorithm for the TNAM with that for the linear quadtree, the theoretical and experimental results show that the former is much more effective than the latter and is a better method to represent the gray images. The algorithm for the TNAM of the gray images is valuable for the theoretical research and potential business foreground.

A Novel Algorithm for Multi-valued Image Representation

Although the representation methods of the quadtree for the multi-valued image have many important applications in different fields, they put too much emphasis upon the symmetry of image segmentation. Therefore, they are not the optimal representation methods. In this paper, inspired by the concept of the packing problem, we present a novel algorithm of binary-bit plane decomposition based on the non-symmetry and anti-packing pattern representation model (BPD-based NAM) for the multi-valued image representation. The theoretical analyses and experimental results presented in this paper show that when the algorithm of BPD-based NAM is compared with that of the popular quadtree, the former can reduce the data storage much more effectively than the latter and it is a better method to represent the multi-valued image. This novel algorithm of BPD-based NAM opens a new research area with respect to the multi-valued image representation and is valuable for the further theoretical research and potential business foreground.

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

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 logn) 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.

A Fractal-based Algorithm for the Metamorphic Animation

The metamorphic technology is one of the most important technologies in the computer animation. This paper presents a new fractal-based algorithm for the metamorphic animation. The conceptual roots of fractals can be traced to the attempts to measure the size of objects for which traditional definitions based on Euclidean geometry or calculus failed. Therefore, the objective of this study is to design a fractal-based algorithm and produce a metamorphic animation based on a fractal idea. The main method we adopted is to weight two IFS (Iterated Function System) codes between the start and the target object by an interpolation function. We test our algorithm on the Matlab software. The experimental results demonstrate that the animation generated according to our method is smooth, natural and fluent. In addition, our algorithm has the merits of stability, efficiency and verisimilitude. This study shows that the fractal idea can be effectively applied in the metamorphic animation. The main feature of our algorithm is that it can deal with a fractal object that the conventional algorithm cannot. In application, our algorithm has many practical values that can improve the efficiency of animation production and simultaneously greatly reduce the cost

A fast algorithm for computing moments of gray images based on NAM and extended shading approach

Computing moments on images is very important in the fields of image processing and pattern recognition. The non-symmetry and anti-packing model (NAM) is a general pattern representation model that has been developed to help design some efficient image representation methods. In this paper, inspired by the idea of computing moments based on the S-Tree coding (STC) representation and by using the NAM and extended shading (NAMES) approach, we propose a fast algorithm for computing lower order moments based on the NAMES representation, which takes O(N) time where N is the number of NAM blocks. By taking three idiomatic standard gray images ‘Lena’, ‘F16’, and ‘Peppers’ in the field of image processing as typical test objects, and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments, the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach, and also the conventional approach are 26.63%, and 82.57% respectively while maintaining the image quality.

A NAM representation method for data compression of binary images

A representation method using the non-symmetry and anti-packing model (NAM) for data compression of binary images is presented. The NAM representation algorithm is compared with the popular linear quadtree and run length encoding algorithms. Theoretical and experimental results show that the algorithm has a higher compression ratio for both lossy and lossless cases of binary images and better reconstructed quality for the lossy case.

A Novel Algorithm Using Non-symmetry and Anti-packing Model with K-Lines for Binary Image Representation

In this paper, we propose a novel algorithm using the NAMK (Non-symmetry and Anti-packing pattern representation Model with K-lines) for the binary image representation. By comparing the algorithm using the NAMK with that using the popular linear quadtree, the theoretical and experimental results presented in this paper show that the former can reduce the data storage much more effectively than the latter and it is a better method to represent the binary image. The algorithm using the NAMK for the binary image representation presented in this paper is valuable for the theoretical research and potential business foregrounds such as decreasing the storage space, increasing the transmission speed, quickening the process procedure, and so forth.

A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure (NAMCEP). By taking some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree (LQT), binary tree (Bintree), non-symmetry and anti-packing model (NAM) with K-lines (NAMK), and NAM representations, we show that NAMCEP can not only reduce the average node, but also simultaneously improve the average compression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algorithm offers significant improvements.

A Direct Non-Symmetry and Anti-Packing Model for Color Images

The representation methods of the hierarchical data structures have been widely applied in many important fields, such as computer visualization, robotics, computer graphics, image processing, and pattern recognition. Since these methods put too much emphasis upon the symmetry of segmentation, they are not the optimal representation methods. In this paper, we present a direct non-symmetry and anti-packing model (DNAM) for color images. Also, we propose a novel algorithm of the DNAM for color images and analyze the data amount of the proposed algorithm. By taking a rectangle subpattern for example, we implement the proposed algorithm and make a comparison with the algorithm of the popular linear quadtree. The theoretical and experimental results presented in this paper show that our proposed algorithm can reduce the data storage much more effectively than that of the linear quadtree and it is a better method to represent color images.

A New HVS-Based Fractal Image Compression Algorithm

Inspired by the traditional quadtree fractal coding algorithm, we put forward a new partition principle of the quadtree according to the feature of HVS. Then we apply the feature of HVS to partition domain and range blocks. Also, we present a new HVS-based search method of finding out the best domain block for each range block in this chapter. Based on the new partition principle of the quadtree and the HVS-based search method, in this chapter, we proposed a new HVS-based fractal image compression algorithm. By comparing our HVS-based algorithm with the basic fractal image compression algorithm, the experimental results show that the encoding speed of our HVS-based algorithm is 8–27 times to that of the basic algorithm when the visual threshold is 30, 60 and 90, respectively, whereas maintaining a satisfactory reconstructed image quality. Therefore, our HVS-based algorithm is an effective image compression method.