中文摘要：

传统提升小波变换无法有效重构遥感图像中的非水平与非垂直高频信息,导致这些地方的高频小波系数仍然较为显著,降低了遥感图像的编码效率。提出了一种新的基于方向优化的提升小波框架（DOLW）。设计基于梯度的方向预测模型获得提升小波的最优变换方向;沿最优变换方向对图像进行先垂直后水平的方向提升变换,削弱遥感图像高频子带中非水平与非垂直方向上的边缘与纹理能量;利用抽样函数完成分数像素上的插值预测。针对遥感图像的实验表明,与传统的提升小波变换相比,新算法获得的重构图像无论峰值信噪比还是主观质量都有显著提高,对今后遥感图像的压缩编码研究具有重要价值。

英文摘要：

The traditional lifting wavelet transform cannot be effectively reconstructed non-horizontal and non- vertical high-frequency information of remote sensing images, which results in these high frequency wavelet coefficients to be still salient relatively and reduces the coding efficiency of remote sensing images. A new lifting wavelet scheme based on direction optimal model called direction optimal lifting wavelet （DOLW） is proposed. The new algorithm first designs a directional prediction model based on gradient to obtain optimal transform direction of lifting wavelet. It executes the directional lifting transform in the direction of the first vertical along the optimal transform direction. The edge and texture energy can be weaken on the non-horizontal and non-vertical direction of the high-frequency subband of the remote sensing image. The new algorithm uses sampling function interpolation to predict the value of sub-pixels. The experimental results for these remote sensing images show that, compared with traditional lifting wavelet transform, the new algorithm improves the peak signal-to-noise ratio （PSNR） and the subjective quality of the reconstructed images significantly. So the new algorithm has important value for the remote sensing image compression and coding in the future.