Science - Based Region - of - Interest Image Compression
نویسندگان
چکیده
As the number of currently active space missions increases, so does competition for Deep Space Network (DSN) resources. Even given unbounded DSN time, power and weight constraints onboard the spacecraft limit the maximum possible data transmission rate. These factors highlight a critical need for very effective data compression schemes. Images tend to be the most bandwidth-intensive data, so image compression methods are particularly valuable. In this paper, we describe a method for prioritizing regions in an image based on their scientific value. Using a wavelet compression method that can incorporate priority information, we ensure that the highest priority regions are transmitted with the highest fidelity. There are three parameters that affect the level of effective compression: the raw data acquisition rate α, the internal buffer size β, and the data transmission rate τ . If λ is the lossless compression coefficient (typically 0.4 to 0.7), then when τ ≥ λα, all of the collected data can be transmitted without loss. Otherwise, some data must be discarded. The compression software being used by the Mars Exploration Rovers, ICER [4], performs wavelet-based compression and prioritizes bit layers based on their contribution to overall image quality. The successor to this technology is ROI-ICER, which prioritizes compressed data based on region-of-interest information [3]. ROI-ICER allocates more transmission bits to areas of the image designated as high-priority. How to best determine the relative priorities of different parts of an image is an open question, and generally must rely on domain-specific information. For example, Dolinar et al. [3] calculated priorities based on temperature information for Earth images to detect information about forest fires.
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