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Adaptively Lossy Image Compression for Onboard Processing

Goodwill, Justin (2020) Adaptively Lossy Image Compression for Onboard Processing. Master's Thesis, University of Pittsburgh. (Unpublished)

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More efficient image-compression codecs are an emerging requirement for spacecraft because increasingly complex, onboard image sensors can rapidly saturate downlink bandwidth of communication transceivers. While these codecs reduce transmitted data volume, many are compute-intensive and require rapid processing to sustain sensor data rates. Emerging next-generation small satellite (SmallSat) computers provide compelling computational capability to enable more onboard processing and compression than previously considered. For this research, we apply two compression algorithms for deployment on modern flight hardware: (1) end-to-end, neural-network-based, image compression (CNN-JPEG); and (2) adaptive image compression through feature-point detection (FPD-JPEG). These algorithms rely on intelligent data-processing pipelines that adapt to sensor data to compress it more effectively, ensuring efficient use of limited downlink bandwidths. The first algorithm, CNN-JPEG, employs a hybrid approach adapted from literature combining convolutional neural networks (CNNs) and JPEG; however, we modify and tune the training scheme for satellite imagery to account for observed training instabilities. This hybrid CNN-JPEG approach shows 23.5% better average peak signal-to-noise ratio (PSNR) and 33.5% better average structural similarity index (SSIM) versus standard JPEG on a dataset collected on the Space Test Program – Houston 5 (STP-H5-CSP) mission onboard the International Space Station (ISS). For our second algorithm, we developed a novel adaptive image-compression pipeline based upon JPEG that leverages the Oriented FAST and Rotated BRIEF (ORB) feature-point detection algorithm to adaptively tune the compression ratio to allow for a tradeoff between PSNR/SSIM and combined file size over a batch of STP-H5-CSP images. We achieve a less than 1% drop in average PSNR and SSIM while reducing the combined file size by 29.6% compared to JPEG using a static quality factor (QF) of 90.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Goodwill, Justinjsg59@pitt.edujsg59
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairGeorge,
Committee MemberMao, Zhi-Hongzhm4@pitt.eduzhm4
Committee MemberDallal, Ahmedahd12@pitt.eduahd12
Date: 29 July 2020
Date Type: Publication
Defense Date: 6 March 2020
Approval Date: 29 July 2020
Submission Date: 19 March 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 59
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Learned Compression, Machine Learning, Artificial Intelligence, Satellite Imagery
Date Deposited: 29 Jul 2020 17:03
Last Modified: 29 Jul 2020 17:03


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