基于逆差金字塔分解的2D-SOFM矢量量化图像压缩

TELSIKS 2005 - 2005 uth International Conference on Telecommunication in ModernSatellite, Cable and Broadcasting Services Pub Date : 2005-09-28 DOI:10.1109/TELSKS.2005.1572160

N. A. Hikal, R. Kountchev

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引用次数: 2

摘要

本文提出了一种新的基于2D-SOFM神经网络的静态图像压缩算法，该算法与逆差分金字塔分解(IDP)方法相对应。该算法适用于逐行图像传输(PIT)。该方法的优点是利用神经网络的学习过程和自适应能力来降低矩阵的计算复杂度和PIT所需的金字塔层总数。此外，对于图像重建，不再需要插值，从而提高了重建图像的质量

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

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 any more, which improves the quality of the reconstructed image

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TELSIKS 2005 - 2005 uth International Conference on Telecommunication in ModernSatellite, Cable and Broadcasting Services

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