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2D-SOFM Vector Quantization for Image Compression Based on Inverse Difference Pyramidal Decomposition

机译:基于反差金字塔形分解的二维SOFM矢量量化​​图像压缩

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In this paper a new developed algorithm for compression of still images based on 2D-SOFM NN's in correspondence with the method of Inverse Difference Pyramid (IDP) decomposition is represented. The new developed algorithm is well suited to be used in Progressive Image Transmission (PIT). Advantage of the method relies on the learning process and adaptation capability of NN's to reduce the matrices computation complexity and the total number of pyramid levels required for PIT. In addition to, for image reconstruction no interpolation is needed anymore, which improves the quality of the reconstructed image.
机译:本文提出了一种新的基于2D-SOFM NN的静态图像压缩算法,该算法与反差金字塔(IDP)分解方法相对应。新开发的算法非常适合用于渐进式图像传输(PIT)。该方法的优势依赖于神经网络的学习过程和适应能力,以减少矩阵计算的复杂性以及PIT所需的金字塔等级总数。另外,对于图像重建,不再需要插值,这提高了重建图像的质量。

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