一种基于小波子带熵的遥感图像压缩算法(英文)

Remote Sensing Image Compression Algorithm Based on Wavelet Sub-bands Entropy

提出了一种使用小波子带熵进行比特分配的遥感图像压缩算法。对遥感图像进行小波提升分解后,分析了各高频子带能量百分比及其熵的变化趋势,在此基础上提出了一种新的快速比特分配方法-使用子带熵进行比特分配。然后对各个高频子带进行均匀量化,量化后的数据采用比特平面编码。对最高比特平面只记录该比特平面中非零系数的坐标,其它比特平面采用行程编码和Huffman编码方法进行压缩。实验结果表明,纹理复杂以及相对平坦的遥感图像使用该算法压缩后都可以获得很好的重构图像质量,峰值信噪比均大于34dB,而压缩比则与图像的复杂程度有关。

A high-performance lossy compression algorithm was proposed for remote sensing image compression based on bit allocation using sub-bands entropy. After decomposing the remote sensing image by using wavelet lifting scheme, the distribution of energy percentage and entropy of high frequency sub-bands were analyzed. A novel bit allocation method using the entropy was proposed. Uniform scalar quantification was implemented for each high frequency sub-band. Bit plane encoding followed and included two parts. The coordinates of non-zero coefficients were registered in the most significant bit plane, and run-length encoding and Huffman encoding were adopted for other bit planes. Experimental results show that the compression scheme performs well on a set of test remote sensing images including complicated texture images and relative planar ones. The Peak Signal to Noise Ratio （PSNR） is all higher than 34dB. However, the Compression Ratio （CR） depends on image＇s complicated degree.