利用格型递归最小二乘滤波器组的高光谱图像压缩

Lossless compression of hyperspectral images using recursive least square lattice filter group

自适应递归最小二乘滤波器具有预测准确、收敛速度快的特点,该滤波器被多种高光谱图像无损压缩方案作为重要组成部分。然而传统递归最小二乘滤波器无法快速找到每个谱带的最优预测长度,其压缩方案的性能有待提升。针对该问题,本文提出基于格型递归最小二乘滤波器组的高光谱图像压缩方案。首先,该方案使用单边高斯预测器对待测像素点做谱带内预测,去除图像的空间相关性。其次,采用格型滤波器组筛选出每个谱带的最优滤波器,获得预测误差。并根据格型滤波器组链式序列更新的特点,简化最优滤波器的筛选过程,大幅度降低计算复杂度。最后对预测误差做算术编码。以AVIRIS 2006高光谱图像为测试数据集,本文算法对16位校准图像、16位未校准图像的平均压缩结果分别为3.34 bits/pixel和5.61 bits/pixel。该算法在获得良好压缩结果的情况下,计算时间低于同类别的其余算法。

The lossless compression algorithms for hyperspectral images based on the recursive least square filter have accurate prediction and fast convergence.However,the traditional compression algorithm is incapable of quickly identifying the optimal prediction length of each band,and the performance of the algorithm needs to be improved.In this paper,we propose a hyperspectral image compression algorithm based on the recursive least square lattice filter group.Considering the spatial correlation of the hyperspectral image,the proposed scheme first uses a single-sided Gaussian predictor for inner-band prediction.The lattice filter group is then used to calculate the optimal filter for each spectral band to obtain the prediction error.Further,the screening process of the optimal filters are simplified according to the characteristics of the chain sequence update of the lattice filter group;this significantly reduces the computational complexity.Finally,the arithmetic code is used to encode the residual data.Experiments on the Airborne Visible/Infrared Imaging Spectrometer(AVIRIS)2006 hyperspectral image datasets reveal that the average compression results of the algorithm for 16-bit calibrated and uncalibrated images are 3.34 bits/pixel and 5.61 bits/pixel,respectively.The proposed algorithm therefore shows a competitive performance with regard to compression and requires less computation time compared to other outstanding algorithms.