基于自适应分类曲线拟合的干涉多光谱图像压缩

Compression of Interference Multispectral Image Based on Adaptive Classification and Curve-Fitting

分析了干涉多光谱图像数据的两个特性,并提出一种基于自适应分类曲线拟合的压缩算法。首先采用均方差准则自适应地将干涉多光谱图像分为强、弱两类干涉区域,并分别构造不同的拟合函数。对强干涉区域,选择典型曲线,并采用最小二乘原理对典型曲线进行拟合,而其余曲线则根据典型曲线进行匹配预测;对弱干涉区域,则分别对所有干涉光强曲线独立进行拟合。最后将所有误差数据进行熵编码。实验结果表明,与JPEG2000相比,该算法能够减少无损压缩输出码率约0.2 bit/pixel,明显提高有损压缩的重建图像质量,降低光谱失真。

By analyzing two characteristics of an interference multispectral image data, a compression algorithm based on adaptive classification and curve-fitting is proposed. The image is partitioned adaptively into intensive interference region and weak interference region by the mean square deviation criterion. Different fitting functions are constructed for the two regions respectively. For the intensive interference region, some typical interference curves are selected to predict other curves, and they are fitted by least square method. For the weak interference region, the data of each interference curve are approximated independently. Finally all the approximating errors of two regions are entropy coded. The experimental results show that, compared with JPEG2000, the proposed algorithm not only decreases the average output bit-rate by about 0.2 bit/pixel for lossless compression, but also improves the reconstructed images and reduces the spectral distortion, especially at high bit-rate for lossy compression.