图象所具有的海量性和无序性的特点，决定了多媒体应用的构建必须解决图象数据的高效压缩和有效检索两个关键问题，而由于传统的压缩和检索技术的研究是相互分离的，因而限制了多媒体应用系统整体性能的提高，针对此问题，从两者相互结合的观点，对图象压缩和检索方法进行了研究，首先在小波变换域内，基于迭代分形对图象数据进行压缩，然后在图象分形码的基础上，利用迭代函数系统分布特性构建的特征量来支持图象检索，实验结果验证了该方法的可行性和有效性，同时也表明了基于迭代分形的图象检索方法所具有的巨大应用潜力。

Due to the enormous magnitude and unstructured contents of multimedia data, solutions must be provided for their effective compression and efficient indexing, in order to realize all kinds of multimedia applications. However, the traditional approaches treat compression and indexing problems separately during the past decades. The compression algorithms are implemented without indexing supported in compressed domain while the indexing operations are mainly undertaken in original format of multimedia data, resulting in lower overall performance of current multimedia application system. In order to improve the situation, a joint image compression indexing algorithm based on iterative fractal method is proposed in this paper. Firstly, the iterative fractal method is employed to compress the image in wavelet domain for effective compression. Then feature vectors representing the distribution properties of IFS(Iterative Function System) are constructed to support the indexing of images based on the fractal coded image data. Simulation results verify the efficiency of the methods and show the potentials of the fractal based image indexing methods.