基于神经网络的自适应矢量量化

Proceedings of Third International Conference on Signal Processing (ICSP'96) Pub Date : 1996-10-14 DOI:10.1109/ICSIGP.1996.566588

Qi Bensheng, Qi Jianqin, AnPin, Zhang Dian-cheng

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引用次数: 0

摘要

首先介绍了一些矢量量化算法。然后，提出了一种基于神经网络的自适应矢量量化图像编码方法。该方法首先将图像分割成一个子图像，并对其进行DCT变换，然后在变换后的域中使用频率敏感竞争学习(FSCL)对其进行分类编码。实验结果表明，该方法没有局部失真，具有较高的压缩比。

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An adaptive vector quantization based on neural network

Some vector quantization algorithm are first surveyed. Then, an adaptive vector quantization method for image coding based on a neural network is proposed. This method first partitions the image into a subimage and transforms them with the DCT, and then classifies and encodes them in the transformed domain using frequency sensitive competitive learning (FSCL). The experimental results show that this VQ method has no local region distortion and a high compression ratio.

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Proceedings of Third International Conference on Signal Processing (ICSP'96)

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