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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >Fast Fractal Image Encoder using NN-based Block Classifier and Improved Isometry Transformation
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Fast Fractal Image Encoder using NN-based Block Classifier and Improved Isometry Transformation

机译：使用基于NN的块分类器和改进的等距变换的快速分形图像编码器

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摘要

This paper proposes a new fractal image encoder using a SOFM neural network based classifier and also an improved isometric transformation, to reduce the encoding time. Here the sizes of a domain block and range block are 8x8 pixels and 4x4 pixels, respectively. Block is classified into one of four patterns, based on the variation of intensities of the pixels in the block: flat where it is very low, middle where it is small, vertical/horizontal where there exists a vertical or horizontal edge, diagonal where there exists a diagonal edge. The SOFM neural network memorizes these patterns by competitive learning where the weights on the connections are determined by the Kohenen's learning rules. To reduce the searching time, the proposed algorithm searches domain blocks following a spiral trajectory starting from the block selected in the range and uses an improved isometric transformation which classifies the templates before comparison. The experimental results have shown that the proposed algorithm reduces the encoding speed by 50% on average while maintaining the same PSNR and bit rate, compared to the other's recent research results.
机译：本文提出了一种使用基于SOFM神经网络的分类器的新型分形图像编码器，以及一种改进的等距变换，以减少编码时间。此处，域块和范围块的大小分别为8x8像素和4x4像素。根据块中像素强度的变化，将块分为四个模式之一：非常低的平面，很小的中间，存在垂直或水平边缘的垂直/水平，存在对角线的对角线存在对角线边缘。 SOFM神经网络通过竞争性学习来记住这些模式，其中连接的权重由Kohenen的学习规则确定。为了减少搜索时间，所提出的算法从范围内选择的块开始沿螺旋轨迹搜索域块，并使用改进的等距变换，在比较之前对模板进行分类。实验结果表明，与其他算法的最新研究结果相比，该算法在保持相同的PSNR和比特率的同时，平均可将编码速度降低50％。

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