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小波域的灰色关联度图像压缩

发布时间:2019-05-12 19:31
【摘要】:为了改善小波变换的图像稀疏表示性能,提出了一种小波域的灰色关联度图像压缩算法.首先,利用小波变换对测试图像进行分解,获得不同区域的小波系数;然后,利用小波系数特点,将灰色关联度用于系数关联度的刻画中,并计算不同尺度间系数的灰色关联度;根据小波系数区域特征,将小波系数进行分类,构造出不同系数类型下的稀疏表示方法;最后,将该算法应用于图像压缩.实验结果表明,在相同压缩率下,所提算法的客观评价指标峰值信噪比较现有同类算法提高了1.04~3.65 d B,图像主观视觉质量明显提高.所提算法能够结合系数特征和视觉特性自适应地构造字典,提高了图像稀疏表示能力,进一步提高了图像压缩性能.
[Abstract]:In order to improve the sparse representation performance of wavelet transform, a gray relational image compression algorithm in wavelet domain is proposed. Firstly, the wavelet transform is used to decompose the test image to obtain the wavelet coefficients in different regions. Then, by using the characteristics of wavelet coefficients, the grey correlation degree is applied to the description of coefficient correlation degree, and the grey correlation degree of coefficients between different scales is calculated. According to the regional characteristics of wavelet coefficients, the wavelet coefficients are classified and sparse representation methods under different coefficient types are constructed. finally, the algorithm is applied to image compression. The experimental results show that under the same compression ratio, the objective evaluation index of the proposed algorithm is 1.04 鈮,

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