The quality of image processing can be significantly improved by exploiting the correlation among image wavelet coefficients and modeling the statistics for these coefficients.
研究图像小波系数间的统计相关性并建立适当的模型，可以显著提高图像处理的质量。
After the clustering characteristics of image wavelet coefficients' significance distribution in each sub-band was analyzed, run-length coding (RLC) was introduced into SPIHT's output stage.
针对图像小波系数在各子带内显著性分布的聚簇特征，提出了在SPIHT算法的输出环节引入游程编码。
For the image high-frequency part, use the new evaluation factor - wavelet neighborhood information to choose the ultimate wavelet high-frequency coefficients.
使用由方差和平均梯度构造的新的评价因子——小波邻域信息量作为融合规则选取小波高频系数。
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