Details
| Original language | English |
|---|---|
| Title of host publication | 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9798350395570 |
| ISBN (print) | 979-8-3503-9558-7 |
| Publication status | Published - 2023 |
| Event | 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023 - Athens, Greece Duration: 31 Oct 2023 → 2 Nov 2023 |
Publication series
| Name | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing |
|---|---|
| ISSN (Print) | 2158-6268 |
| ISSN (electronic) | 2158-6276 |
Abstract
For storing or transmitting hyperspectral images (HSI) in drone remote sensing, an efficient data compression with low computational cost has to be done onboard. Many scenarios do not allow any loss of information except noise which is not interpreted as information. We present an HSI data compression scheme using H.265/HEVC Main10 Profile Hardware, already integrated on the camera system of a drone. Using reference software, we determine, for each test data investigated, the so called best quantization step size which holds the constraint of loosing no information at the smallest possible data amount. We map the analog sensor gain to the best quantization step size and find a linear dependancy which allows a correct setting of the quantization step size in real-time. Finally, we verify the conformity of the reference software used for the investigations with hardware simulation results. We achieve compression ratios between 11 and 24.
Keywords
- coding, data compression, drone remote sensing, HEVC, hyperspectral imaging, quantization
ASJC Scopus subject areas
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Signal Processing
Cite this
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2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE Computer Society, 2023. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Gain Adapted Quantization in HEVC Coding Applied to Drone Remote Sensing
AU - Pestel-Schiller, Ulrike
AU - Meinicke, Paul Robert
AU - Ostermann, Jorn
AU - Busch, Johannes
PY - 2023
Y1 - 2023
N2 - For storing or transmitting hyperspectral images (HSI) in drone remote sensing, an efficient data compression with low computational cost has to be done onboard. Many scenarios do not allow any loss of information except noise which is not interpreted as information. We present an HSI data compression scheme using H.265/HEVC Main10 Profile Hardware, already integrated on the camera system of a drone. Using reference software, we determine, for each test data investigated, the so called best quantization step size which holds the constraint of loosing no information at the smallest possible data amount. We map the analog sensor gain to the best quantization step size and find a linear dependancy which allows a correct setting of the quantization step size in real-time. Finally, we verify the conformity of the reference software used for the investigations with hardware simulation results. We achieve compression ratios between 11 and 24.
AB - For storing or transmitting hyperspectral images (HSI) in drone remote sensing, an efficient data compression with low computational cost has to be done onboard. Many scenarios do not allow any loss of information except noise which is not interpreted as information. We present an HSI data compression scheme using H.265/HEVC Main10 Profile Hardware, already integrated on the camera system of a drone. Using reference software, we determine, for each test data investigated, the so called best quantization step size which holds the constraint of loosing no information at the smallest possible data amount. We map the analog sensor gain to the best quantization step size and find a linear dependancy which allows a correct setting of the quantization step size in real-time. Finally, we verify the conformity of the reference software used for the investigations with hardware simulation results. We achieve compression ratios between 11 and 24.
KW - coding
KW - data compression
KW - drone remote sensing
KW - HEVC
KW - hyperspectral imaging
KW - quantization
UR - http://www.scopus.com/inward/record.url?scp=85186271317&partnerID=8YFLogxK
U2 - 10.1109/WHISPERS61460.2023.10430623
DO - 10.1109/WHISPERS61460.2023.10430623
M3 - Conference contribution
AN - SCOPUS:85186271317
SN - 979-8-3503-9558-7
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
PB - IEEE Computer Society
T2 - 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023
Y2 - 31 October 2023 through 2 November 2023
ER -