基于经验数据分解的干涉多光谱图像压缩算法

Compression of interferential multispectral images based on empirical data decomposition

针对干涉多光谱图像数据的非平稳特性,提出一种经验数据分解的图像压缩算法.经验数据分解利用干涉曲线数据的局部特性和变化规律,将其分解为局部区域数据和值以及差值数据,从而实现对非平稳数据的多分辨率分析.本压缩算法首先利用经验数据分解方法去除干涉多光谱图像数据的相关性,并提出对应的二维多级分解结构.最后对分解系数采用改进的EBCOT算法进行编码.实验结果表明,与JPEG2000标准相比,本算法在无损压缩时输出码率平均下降0.15比特/像素,而有损压缩的重建图像质量提高1.1~2.5 dB,同时降低恢复光谱的相对二次误差,有效的保护了光谱信息.

Due to the non-stationary property of interferential multispectral image data, a novel compression algorithm for interferential multispectral images with proposed Empirical Data Decomposition （EDD） is presented. EDD can make a multi-resolution analysis of the non-stationary interferential data. With its local characteristic and variation tendency, the non-stationary interferential data are decomposed by EDD into two parts： the sum of local region data and the difference data. In this paper, EDD is first utilized for interferential multispectral image data de-correlation, and a corresponding 2-D decomposition structure is presented as well. The decomposition coefficients are finally coded with the modified EBCOT. Experimental results show that, compared with the JPEG2000 standard, the proposed algorithm decreases the average output ratio by about 0. 15 bit/pixel for lossless compression, and improves the reconstructed images by 1.1 -2.5 dB. The algorithm also reduces the Relative spectral Quadratic Error（RQE） and protects the spectral information efficiently.