Onboard low-complexity compression of solar images

Acquiring and processing astronomical images is becoming increasingly important for accurate space weather prediction and expanding our understanding about the Sun and the Universe. These images are often rich in content, large in size and dynamic range. Efficient, low-complexity compression solutions are essential to reduce onboard storage, processing, and communication resources. Distributed compression is a promising technique for onboard coding of solar images by exploiting correlation between successively acquired images. In this paper we propose an adaptive distributed compression solution using particle filtering that tracks correlation, as well as performing disparity estimation, at the decoder side. The proposed algorithm is tested on the stereo solar images captured by the twin satellites system of NASA's STEREO project. Our experimental results show the significant PSNR improvement over traditional separate bit-plane decoding without dynamic correlation and disparity estimation.