一种适合星上应用的遥感图像有损压缩算法

A lossy compression algorithm of remote sensing image suited to space-borne application

提出了一种高质量的遥感图像有损压缩算法,在分析了遥感图像经Daubechies双正交小波基D5/3整数小波变换后各子带小波系数统计特性和能量分布的基础上,引入了人类视觉特性,用它来控制算法中的量化方案。根据能量的大小确定不同子带对于目标识别的重要程度,选择不同的量化阈值和量化步长进行量化,并对量化后的数据采用固定比特平面编码。仿真实验表明,该算法对于不同内容和纹理的图像,在一定的压缩比下,均获得了PSNR(峰值信噪比)>30dB的恢复图像,在不损失最低频信息的同时较好地保持了遥感图像中丰富的高频信息,实现了高质量的图像压缩,并且算法简便,快捷,所占用的存储容量小,易于硬件实现,适合于星上应用,减少了在遥感图像压缩中小目标的丢失。

A high quality lossy compression algorithm of remote sensing image was presented. After decomposition of the remote sensing image by D5/3 integer wavelet transform and analysis of the statistical characteristic and the energy distribution of wavelet coefficients in each sub-band, the human vision properties are introduced to control the quantification scheme of the algorithm. The important degree for target recognition is determined according to energy in each sub-band, and the different quantification threshold values and the quantification steps are chosen in the quantification. Finally, quantified data are coded in fixed bit-plane. The simulation experiment shows that this algorithm acquires the restoration images of PSNR（Peak Signal Noise Radio）〉30 dB for all images of different contents and texture with certain CR, which keeps much high frequency information of the remote sensing image to realize the high quality image compression. Because of its simplicity, fastness, and small storage, the algorithm is easy to be realized in hardware and suitable for space-borne application.