Reducing Data Dependencies in the Feedback Loop of the CCSDS 123.0-B-2 Predictor

Abstract

On-board multi- and hyperspectral instruments acquire large volumes of data that need to be processed with the limited computational and storage resources. In this context, the CCSDS 123.0-B-2 standard emerges as an interesting option to compress multi- and hyperspectral images on-board satellites, supporting both lossless and near-lossless compression with low complexity and reduced power consumption. Nonetheless, the inclusion of a feedback loop in the CCSDS 123.0-B-2 predictor to support near-lossless compression introduces significant data dependencies that hinder real-time processing, particularly due to the presence of a quantization stage within this loop. This work provides an analysis of the aforementioned data dependencies and proposes two strategies aiming at maximizing throughput in hardware implementations and thus enabling real-time processing. In particular, through an elaborate mathematical derivation, the quantization stage is removed completely from the feedback loop. This reduces the critical path, which allows for shorter initiation intervals in a pipelined hardware implementation and higher throughput. This is achieved without any impact in the compression performance, which is identical to the one obtained by the original data flow of the predictor.