Removing Data Dependencies in the CCSDS 123.0-B-2 Predictor Weight Updating

Yubal Barrios, Joan Bartrina Rapesta, Miguel Hernández-Cabronero, Antonio José Sánchez, Ian Blanes Garcia, Joan Serra Sagristà, Roberto Sarmiento

Producción científica: Contribución a una revistaArtículoInvestigaciónrevisión exhaustiva

Resumen

The Consultative Committee for Space Data Systems (CCSDS) first standardized near-lossless coding capabilities in the CCSDS 123.0-B-2 algorithm. However, this standard does not describe strategies to produce high-throughput hardware implementations, which are not trivial to derive from its definition. At the same time, throughput optimizations without significant compression performance penalties are paramount to enable real-time compression on-board next-generation satellites. This work demonstrates that the weight update stage of the CCSDS 123.0-B-2 predictor can be selectively bypassed to enhance throughput for both lossless and near-lossless modes with minimal impact on compression performance and still produce fully compliant bitstreams. Skipping the weight update implies that those weights must be carefully chosen outside the original CCSDS 123.0-B-2 pipeline. Two strategies are proposed to select effective weight values based on whether a priori information about the current image is exploited or not. Comprehensive experimental results are presented for both proposed strategies and for lossless and near-lossless regimes, using a representative set of hyperspectral images. The coding penalty is, on average, 1% for lossless and 8% for near-lossless, depending on the strategy used to set the initial weights. The proposed method obtains a maximum throughput of one processed sample per clock cycle when it is evaluated using high-level synthesis (HLS), consuming 4.6% of the look-up tables (LUTs) and 31.1% of the internal memory on a Xilinx Kintex UltraScale space-grade field programmable gate array (FPGA).
Idioma originalInglés
Número de artículo5502905
Páginas (desde-hasta)1-5
Número de páginas5
PublicaciónIEEE Geoscience and Remote Sensing Letters
Volumen21
DOI
EstadoPublicada - 2024

Huella

Profundice en los temas de investigación de 'Removing Data Dependencies in the CCSDS 123.0-B-2 Predictor Weight Updating'. En conjunto forman una huella única.

Citar esto