基于混沌与快速小波变换的多光谱图像压缩加密算法

针对多光谱图像存储和传输安全性问题，提出一种将混沌思想、小波变换和KL（karhunen-loeve）变换相结合的多光谱图像压缩加密算法。首先，采用K-means聚类方案将多光谱图像聚类为通用像素，通过选择合适的K值使算法的性能最优，同时便于后续处理；然后对通用像素进行二维离散9/7小波变换，对变换后的系数进行Arnold变换以及加密处理，消除多光谱图像大部分空间冗余，减少压缩过程中的块效应；之后对产生的小波系数进行改进的KL变换，消除残余空间冗余和光谱冗余；最后采用差分脉冲滤波器对系数进行编码，并采用Tent映射对码流进行混淆扩散加密。通过实验可知，本算法的信息熵达到11.794 3（选取12位多光谱图像），信息熵更接近最大值12，优于现有算法，可以更好的隐藏原图特征；该算法的像素变化率（NPCR）和归一化平均变化强度(UACI)分别为99.81%和34.19，优于现有的其他算法，本算法可以更好的抵御差分攻击；输出比特流变化率保持在47.62%～47.71%之间，密文比特流变化率保持在47.45%～47.52%，本算法具有较好的密钥敏感性；在压缩比为4∶1～32∶1范围内，系统PSNR在42 dB以上，具有很高的压缩性能。在4∶1～32∶1范围内，本压缩算法达到很高的峰值信噪比，优于现有的压缩算法，在正常工作压缩比为16∶1时，比现有压缩算法的信噪比提高了0.64 dB以上。为进一步验证算法在高压缩比情况下的压缩性能，该研究测试了压缩比为128∶1时系统的信噪比为31.28，此时，重建后的图像较为清晰，优于现有算法1 dB以上。可见，该算法可行，且特别适合对压缩比要求较高的场合，并在频谱保真方面具有较好的效果。

Multispectral Image Compression and Encryption Algorithm Based on Chaos and Fast Wavelet Transform

A multispectral image compression and encryption algorithm that combines chaos, wavelet transform and KL transform is proposed for solving the security problem of multi-spectral image compression and transmission. Firstly, the K-means clustering scheme is used to cluster multi-spectral images into common pixels, and the performance of the algorithm is optimized by selecting the appropriate K value, and it is convenient for subsequent processing. Secondly, the multispectral image is clustered into general pixels, we will perform a two-dimensional discrete 9/7 wavelet transform on the general pixels, and then perform Arnold transform and encryption processing on the transformed coefficients to eliminate most of the spatial redundancy of the multispectral image and reduce the block effect of the compression process. Next, to eliminate residual spatial redundancy and spectral redundancy, the generated wavelet coefficients are performed by KL transform. Finally, differential pulse filters are used to encode the coefficients, and Tent mapping is used to implement confusion diffusion encryption on the code stream. Through experiments, it can be known that the information entropy of this algorithm reaches 11.794 3 (selecting 12-bit multispectral images), and the information entropy is closer to the maximum value of 12, which is better than the existing algorithm and can better hide the original image features. The NPCR and UACI are respectively 99.81% and 34.19, which are better than the existing other algorithms, which can better resist differential attacks. The output bit-stream change rate is maintained between 47.62%～47.71%, and the ciphertext bitstream change rate is maintained between 47.45%～47.52%, so this algorithm has good key sensitivity; In the range of 4∶1～32∶1, the system PSNR is above 42 dB, which has high compression performance. Within the range of 4∶1～32∶1, this compression algorithm achieves a very high peak signal-to-noise ratio, which is better than the existing compression algorithm. When the normal working compression ratio is 16∶1, it is better than the existing compression algorithm. The ratio is improved by more than 0.64 dB. In order to further verify the compression performance of the algorithm in the case of a high compression ratio, this paper tested the system’s signal-to-noise ratio of 31.28 when the compression ratio is 128∶1. The reconstructed image is clearer at this time, which is more than 1dB better than the existing algorithm. It can be seen that this algorithm is feasible and particularly suitable for occasions which require a high compression ratio and has a good effect in terms of spectrum fidelity.