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Fractal image compression and recurrent iterated function systems

机译:分形图像压缩和循环迭代功能系统

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Linear fractal models such as the iterated function system (IFS), recurrent IFS (RIFS), and Lindenmeyer system (L-system) concisely describe complex objects using self-reference. These models hold much promise in computer graphics as geometric representations of detail. The use of fractals for image compression sacrifices its fractal origins in the search for optimal coding gain. This article rebuilds the relationship between the fields of fractal image compression (FIC) and fractal geometry to better facilitate the sharing of new results. Many geometric representations exist for smooth shapes, and each has certain benefits and drawbacks. Computer-aided geometric design has produced many algorithms to convert a given curve or surface description into the most appropriate geometric representation for a given task. Likewise, there are several models for linear fractal shapes and several methods for converting between the representations. The representation used by FIC has been called partitioned IFS or local IFS. This article describes a method for converting FIC's partitioned/local IFS to fractal geometry's RIFS. This conversion algorithm allows FIC to represent any input shape as a linear fractal and permits algorithms developed for linear fractals to be applied to a wider variety of shapes. When every domain element can be expressed as the union of range elements, FIC produces a structure that is equivalent to a RIFS, enabling it to automatically model arbitrary shapes.
机译:线性分形模型,例如迭代函数系统(IFS),递归IFS(RIFS)和Lindenmeyer系统(L-system),使用自引用简洁地描述了复杂的对象。这些模型在计算机图形学中以几何图形表示细节很有希望。使用分形进行图像压缩会在寻找最佳编码增益时牺牲其分形起源。本文重建了分形图像压缩(FIC)字段和分形几何之间的关系,以更好地促进新结果的共享。存在许多用于平滑形状的几何表示,每种都有某些优点和缺点。计算机辅助几何设计已经产生了许多算法,可以将给定的曲线或曲面描述转换为最适合给定任务的几何表示。同样,有一些线性分形形状的模型和几种表示形式之间转换的方法。 FIC使用的表示形式称为分区IFS或本地IFS。本文介绍了一种将FIC的分区/局部IFS转换为分形几何的RIFS的方法。这种转换算法允许FIC将任何输入形状表示为线性分形,并且允许针对线性分形开发的算法可以应用于更广泛的形状。当每个域元素都可以表示为范围元素的并集时,FIC会生成与RIFS等效的结构,从而使其能够自动对任意形状进行建模。

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