【作者】 杨静；

【导师】 何传江；

【作者基本信息】 重庆大学 ， 分形理论及应用， 2005， 硕士

【摘要】 随着多媒体技术的不断发展和现代信息社会对通信业务要求的不断增长,图像压缩编码已成为正在建设的数字信息化社会所依赖的主要技术基础之一。在现有的图像压缩方法中,分形图像压缩编码是一种很有前途的编码方法。十余年来,它以新颖的思想、高压缩比、分辨率无关性和快速解码等优点得到国内外学者的广泛关注。但是,编码时间过长是这种方法进一步实用化的一个障碍。针对这个问题,本学位论文进行了一些研究,并提出了一个快速分形编码算法。本学位论文的主要内容如下: 第1 章简单介绍几种著名的图像压缩方法,如预测编码、变换编码、矢量量化编码、小波变换编码和分形编码等。第2 章简要介绍分形编码必需的一些基本数学知识,如度量空间、不动点定理、拼贴定理以及迭代函数系统。第3 章介绍基本分形编码算法的原理、算法的描述与实现以及实验结果的讨论。第4 章介绍静态灰度图像的分形编码的研究概况和发展趋势,重点介绍了基于小波的混合分形编码算法的研究概况。第5 章提出了一个快速分形编码算法,它基于作者提出的图像块的形态特征的概念。该算法将Range-Domain 块匹配问题转化为在形态特征意义下的最近邻搜索问题,大大减少了搜索空间,从而大大加快了编码速度。第6 章全文总结和作者进一步的研究方向。更多还原

【Abstract】 With the development of multimedia technology and the increase of communication requirement, image coding has become one of the key technologies on which the digital society depends. Fractal image coding is a very promising image compression technique. It has been receiving more attention in research over the past decade because of its novelty, high compression rate, resolution independence and fast decoding; however, it requres a very long encoding time. The dissertation thus proposed a new fractal coding algorithm to accelerate encoding process. The main contents described in this dissertation are as follows: Chapter 1 is the introduction, which consists of an overview of several well-known image compression techniques, such as predictive coding, transform coding, vector quantization, wavelet coding, baseline fractal coding, and so on. Chapter 2 introduces the necessary mathematical knowledge of fractal image coding, including metric space, Banach’s fixed-point theorem, collage theorem and iterated function systems (IFS). Chapter 3 describes the basic principle of fractal image coding, gives the description and implementation of the baseline fractal coding algorithm, and discusses experimental results. Chapter 4 presents a survey on the current literatures of fractal image coding for still grayscale image, especially wavelet-fractal image coding. Chapter 5 is the key part, in which the author proposed a fast fractal encoding algorithm based on the shape-feature of an image block, which is defined by the author. The proposed algorithm converted the range-domain matching problem to the nearest neighbors search problem in the sense of shape feature. The search space is reduced greatly, so the encoding time is considerably less than that of the baseline fractal encoding algorithm. Chapter 6 gives the conclusions and the direction of the author’s future research.更多还原