Desenvolvimento e implementação em FPGA de um compressor sem perdas de baixa complexidade para imagens de satélite

• Autor(a) principal: Costa, Yuri Gonzaga Gonçalves da
• Data de Publicação: 2012
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFPB
• Texto Completo: https://repositorio.ufpb.br/jspui/handle/tede/6071
• topic: Imagens de Satélite; Sensoriamento Remoto; Compressão de Dados; Predição; Codificação de Golomb-Rice; Verificação Funcional; FPGA; Satellite Images; Remote Sensing; Data Compression; Golomb-Rice Coding; Prediction; Functional Verification; FPGA; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

The amount of data generated and transmitted by satellites to ground stations is always growing. As the technology advances, space imaging systems, especially those present in Earth observing missions, use equipment of increasing resolutions. Hence, it is necessary to ensure that this great quantity of data arrives at their destination reliably. Among some techniques involved, data compression plays an important role to accomplish this requirement. A data compression system for this purpose must comply with some conditions, particularly regarding performance. In this context, hardware implementations based on prediction and Golomb-Rice coding has achieved excellent results considering hardware and compression performance in both lossless and lossy cases. This work proposes a digital hardware approach of a low complexity satellite image lossless compressor based on prediction and Golomb-Rice coding that is attuned to the balance between performance requirements and error propagation, a common issue in space systems environment that is enhanced by data compression. In order to validate and analyze the compressor, a functional verification and FPGA prototyping methodology were followed. Given an image set from Brazilian's National Institute for Space Research (INPE, in the Portuguese acronym), CBERS-2B satellite, its results in FPGA show that this compressor achieves average compression ratio of 3.4, comparable value to related works in this area, and throughput of 28 MPixel/s (224 Mbit/s). Taking advantage of images nature, its compression can be parallelized through simultaneous multi-cores compressors. For example, using 5 cores, this work is able to compress those images in a rate of 142 MPixel/s (1.1 Gbit/s). All these features make it useful and effective in a current remote sensing imaging system.