Semantic progressive transmission for deep space communications

Summary form only given. We present an integrated system for the intelligent progressive transmission of data for deep space communications. This work is motivated by the realization that much more information can be collected by imaging and remote sensing equipment than can be transmitted through downlink channels. The goal of this work was to extend the idea of progressive transmission by incorporating semantic value to the “importance” attributes of encoded data. In particular, we define a simple measure of science return based on information theoretic considerations as well as on the estimated scientific value of the transmitted data. The task of determining the relative scientific importance of segments of data is carried out by an onboard science processing module, designed according to guidelines provided by the remote user (the science community). This module pre-processes the image and provides input to the progressive encoder in the form of a suitable “classification map”. By combining the semantic characterization produced by the science processing module with the content-blind data organization criteria of the traditional progressive encoder, we obtain a new measure of the importance of each segment of data. Algorithms that best utilize the available resources can thus be designed and their performances in terms of science return assessed