Remote Sensing Image Compression Evaluation Method Based on Neural Network Prediction and Fusion Quality Fidelity

Abstract

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.