Research on optimization of CCSDS compression algorithm based on snapshot mosaic hyperspectral images

Targeting the deficiencies of the full prediction mode and simplified prediction mode provided by the CCSDS 123.0-B-2 compression standard of the International Committee for Space Data Systems, this paper proposed a novel hybrid prediction mode combining the full prediction mode and the simplified prediction mode, and optimizes the local sum and local difference calculation parts of the predictor. The proposed hybrid prediction mode reduces the prediction complexity of edge pixels while ensuring the compression effect, which is favorable for deployment on FPGA hardware systems. The improved compression algorithm was tested and analyzed using a 25-channel hyperspectral image captured by a self-made snapshot mosaic hyperspectral imager developed by our research team. The experimental results show that the improved algorithm can realize the lossless compression of snapshot mosaic hyperspectral images (SSM HSI) with the compression effect of 2.67 ~ 4.32 bits/sample and the compression ratio of 1.86 ~ 3.01. The hybrid prediction mode can be applied in the fields of real-time imaging of unmanned aircraft-carried hyperspectral and wireless transmission of large-capacity hyperspectral images.