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Adaptive partitionings for fractal image compression

机译:分形图像压缩的自适应分区

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In fractal image compression a partitioning of the image into ranges is required. Saupe and Ruhl (1996) proposed to find good partitionings by means of a split-and-merge process guided by evolutionary computing. In this approach ranges are connected sets of small square image blocks. Far better rate-distortion curves can be obtained as compared to traditional quadtree partitionings, however, at the expense of an increase of computing time. In this paper we show how conventional acceleration techniques and a deterministic version of the evolution reduce the time-complexity of the method without degrading the encoding quality. Furthermore, we report on techniques to improve the rate-distortion performance and evaluate the results visually.
机译:在分形图像压缩中,需要将图像分隔成范围。 Saupe和Ruhl(1996)拟议通过进化计算所指导的分裂和合并过程来找到良好的分配。在这种方法中,范围是连接的小方形图像块集。然而,与传统的Quadtree分区相比,可以获得更好的速率 - 失真曲线,但是,以牺牲计算时间的增加而相比,可以获得。在本文中,我们展示了传统的加速技术和何种求解的确定性版本降低了方法的时间复杂性而不会降低编码质量。此外,我们报告了提高速率失真性能的技术,并在视觉上评估结果。

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