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Lossless image compression with autosophy networks

机译:使用自体网络进行无损图像压缩

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Abstract: science of Autosophy explains `self-assembling structures,' such crystals or living trees, in mathematical terms. This research has produced a mathematical theory of `learning' and a new `information theory' which permits the growing of self-assembling data network in a computer memory similar to the growing of `data crystals' or `data trees' without data processing or programming. Self-growing Autosophy networks yield real-time `lossless' image compression in which the transmission bandwidth is independent of screen size, resolution, or scanning rates. The systems contain a peculiar self-growing omni dimensional image library in which many image fragments are stored in mathematical hyperspace. For transmission each input image is broken into fragments of various sizes by comparing it like a jigsaw puzzle with the largest matching fragments in the library. The fragments ar identified with a `pattern address' and a `location address' and transmitted as `superpixel.' Each superpixel may represent any size part of the image, from single pixels to entire screen images. For storage compression each image is reduced into a single output code to the computer. The image information is stored in a mathematical hyperspace to yield many orders of magnitude lossless image compression. !12
机译:摘要:Autosophy科学用数学术语解释了“自组装结构”,例如晶体或活树。这项研究产生了“学习”的数学理论和新的“信息论”,该理论允许在计算机内存中自组装数据网络的增长,类似于无需数据处理或数据树或数据树的增长。编程。自增长的Autosophy网络可产生实时的“无损”图像压缩,其中传输带宽与屏幕尺寸,分辨率或扫描速率无关。该系统包含一个特殊的自增长全维图像库,其中许多图像片段存储在数学超空间中。为了进行传输,将每个输入图像像拼图游戏一样与库中最大的匹配片段进行比较,将其分成各种大小的片段。片段以“模式地址”和“位置地址”标识并作为“超像素”发送。从单个像素到整个屏幕图像,每个超像素都可以代表图像的任何大小部分。为了进行存储压缩,每个图像都被简化为计算机的单个输出代码。图像信息存储在数学超空间中,以产生许多数量级的无损图像压缩。 !12

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