An Extremely Pipelined FPGA Implementation of a Lossy Hyperspectral Image Compression Algorithm

Segmented and pipelined execution has been a staple of computing for the past decades. Operations over different values can be carried out at the same time speeding up computations. Hyperspectral image compression sequentially processes samples, exploiting local redundancies to generate a predictabl...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 58; číslo 10; s. 7435 - 7447
Hlavní autoři: Bascones, Daniel, Gonzalez, Carlos, Mozos, Daniel
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Compression
Data transmission
Encoding
Field programmable gate arrays
FPGA
Hardware
hyperspectral image
Hyperspectral imaging
Image coding
Image compression
Mathematics
pipeline
Real-time systems
Submarine pipelines
ISSN:0196-2892, 1558-0644
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Shrnutí:Segmented and pipelined execution has been a staple of computing for the past decades. Operations over different values can be carried out at the same time speeding up computations. Hyperspectral image compression sequentially processes samples, exploiting local redundancies to generate a predictable data stream that can be compressed. In this article, we take advantage of a low complexity predictive lossy compression algorithm which can be executed over an extremely long pipeline of hundreds of stages. We can avoid most stalls and maintain throughput close to the theoretical maximum. The different steps operate over integers with simple arithmetic operations, so they are especially well-suited for our FPGA implementation. Results on a Virtex-7 show a maximum frequency of over 300 MHz for a throughput of over 290 MB/s, with a space-qualified Virtex-5 reaching 258 MHz, being five times as fast as the previous FPGA designs. This shows that a modular pipelined approach is beneficial for these kinds of compression algorithms.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.2982586