基于方向角预测三维小波变换的干涉多光谱图像压缩

Three-Dimensional Orientation Prediction-Based Wavelet Transform for Interference Multi-Spectral Images Compression

根据大孔径静态干涉多光谱图像的成像特点,提出一种基于方向角预测的三维小波变换。这种三维小波变换的新颖之处在于它将方向预测结合到三维提升小波中。这样每次提升小波中的预测可在相关性最强的方向上进行而不需总是局限在变换的方向上。实验证明,对于干涉多光谱图像这种方向性很强的立体图像序列,基于方向角预测的三维小波变换比原始三维提升小波变换有明显改进,在相同的量化编码下,基于方向角预测的三维小波变换比原始三维提升小波变换提高1dB左右。经该种方法压缩的图像的光谱特性也得到明显改善。

According to the characteristics of large aperture static imaging spectrometer （LASIS）, a three-dimensional （3D） orientation prediction-based wavelet transform method is proposed. The novelty of this method is that it combines directional prediction into 3D-wavelet. In this way, the prediction selects pixel in the most correlative direction and is not limited on the prediction direction. The experimental results show that this 3D orientation prediction wavelet improves the performance of wavelet obviously particularly on LASIS image with quite severe direction. Followed with the same quantization and coding methods, the proposed method outperforms the original 3D lifting wavelet by 1 dB, Meanwhile, the spectrums recovered by the proposed method also possesses better performance.