基于干涉图加权叠加的图像压缩算法

Image Compression Algorithm Based on the Interference Figure Weighted Superposition

在对图像进行压缩的过程中,容易出现信息丢失的情况,导致传统图像压缩算法由于相关性低的图像也可参与等权计算,使得图像产生偏差及失真,无法有效实现图像压缩,提出一种基于干涉图加权叠加的图像压缩算法,给出高相关点的平均形变相位变化速率,依据误差传播定律,求出全部干涉图叠加后高相关点受到的大气延迟干扰,对每个图像对应的相关系数进行计算,依据模型采集高相关目标点,干涉图被叠加后,给出高相关点大气延迟对线性形变速率的干扰,通过移位操作获取样本的Exp-Golomb级数,完成编码获取数据的非负映射操作,利用上一个样本的Exp-Golomb编码级数对当前样本值级数进行估测,通过计算原始干涉图数据和压缩后的干涉图数据的压缩比与峰值信噪比,对压缩效果进行度量。通过光谱相对均方误差RQE对压缩前后的原始光谱和复原光谱进行度量。仿真实验结果表明,所提方法具有很高的精度。

In the process of image compression, prone to the condition of the missing information, the traditional image compression algorithms can also be right to participate in such as the image with low correlation calculation, makes the deviation and distortion image, unable to effectively realize the image compression, put forward a kind of image compression algorithm based on weighted superposition interference figure, gives relevant points higher average deformation phase rate of change, according to the law of error propagation, and the superposition of all interference figure after the relevant points higher disturbance by the atmospheric delay, for each image corresponding to the correlation coefficient is calculated, based on the model to collect related target high, after interference figure is superposition, gives relevant points higher linear deformation rate of atmospheric delay, by shift operation to obtain samples of Exp-Golomb series, complete code to get the data of nonnegative mapping operations, using a sample of Exp- Golomb codes to evaluate series for the current sample value by calculating the raw interferogram data and compressed interferogram data compression ratio and peak signal to noise ratio, compression effect of measurement.Through the spectrum before and after the compression RQE relative mean square error of the original spectra and spectral measurement.The simulation results show that the proposed method has high precision.