Lossy compression can produce false information, such as blockiness, noise, ringing, ghosting, aliasing, and blurring. This paper provides a comprehensive model for optical remote sensing image characteristics based on the block standard deviation’s retention rate (BSV). We first propose a compression evaluation method, CR_CI, that combines neural network prediction and remote sensing image quality fidelity. Through the compression evaluation and improved experimental verification of multiple satellites (CBERS-02B satellite, ZY-1-02C satellite, CBERS-04 satellite, GF-1, GF-2, etc.), CR_CI can be stable, cleverly test changes in the information extraction performance of optical remote sensing images, and provide strong support for optimizing the design of compression schemes. In addition, a predictor of remote sensing image number compression is constructed based on deep neural networks, which combines compression efficiency (compression ratio), image quality, and protection. Empirical results demonstrate the image’s highest compression efficiency under the premise of satisfying visual interpretation and quantitative application.

中文翻译：

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基于神经网络预测和融合质量保真度的遥感图像压缩评价方法

有损压缩会产生虚假信息，例如块状，噪声，振铃，重影，混叠和模糊。本文基于块标准差的保留率（BSV），为光学遥感图像特征提供了一个综合模型。我们首先提出一种结合神经网络预测和遥感图像质量保真度的压缩评估方法CR_CI。通过对多颗卫星（CBERS-02B卫星，ZY-1-02C卫星，CBERS-04卫星，GF-1，GF-2等）进行压缩评估和改进的实验验证，CR_CI可以稳定，巧妙地测试光学遥感图像的信息提取性能，为优化压缩方案设计提供了有力的支持。此外，基于深度神经网络构造了遥感图像数量压缩的预测器，该神经网络将压缩效率（压缩比），图像质量和保护结合在一起。实验结果表明，在满足视觉解释和定量应用的前提下，图像具有最高的压缩效率。