Synthetic hyperspectral array video database with&#x00A0;applications to cross-spectral reconstruction and&#x00A0;hyperspectral video coding

Frank Sippel; Jürgen Seiler; André Kaup

doi:10.1364/JOSAA.479552

Journal of the Optical Society of America A
Vol. 40,
Issue 3,
pp. 479-491
(2023)
•https://doi.org/10.1364/JOSAA.479552

Synthetic hyperspectral array video database with applications to cross-spectral reconstruction and hyperspectral video coding

Frank Sippel, Jürgen Seiler, and André Kaup

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Frank Sippel, Jürgen Seiler, and André Kaup, "Synthetic hyperspectral array video database with applications to cross-spectral reconstruction and hyperspectral video coding," J. Opt. Soc. Am. A 40, 479-491 (2023)

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Related Topics
Table of Contents Category
- Color, Spectroscopy, and Spectral Imaging
Optics & Photonics Topics
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The topics in this list come from the Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: October 31, 2022
- Revised Manuscript: January 16, 2023
- Manuscript Accepted: January 17, 2023
- Published: February 15, 2023

Abstract

In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground-truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in peak signal-to-noise ratio (PSNR) of up to 5.6 dB dependent on the scene. Second, a hyperspectral video coder is introduced, which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene.

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Supplementary Material (1)

Name	Description
Dataset 1	The proposed synthetic hyperspectral video dataset.

Data availability

Data underlying the results presented in this paper are available in Dataset 1, Ref. [48].

48. F. Sippel, J. Seiler, and A. Kaup, “FAU-LMS / HyViD: a synthetic hyperspectral array video database,” GitHub (2022), https://github.com/FAU-LMS/HyViD.

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Database	Spatial Res.	# Channels	Wavel. Area (nm)	# Images	Videos	Rerendering	Cam. Array	Depth
CAVE [16]	$512 \times 512$	31	400–700	32	✗	✗	✗	✗
UGR [17]	$1000 \times 900$	61	400–1000	14	✗	✗	✗	✗
Hordley [18]	$\sim 336 \times 271$	31	400–700	23	✗	✗	✗	✗
Le Moan [19]	$500 \times 500$	160	410–1000	9	✗	✗	✗	✗
Foster [20]	$1018 \times 1339$	33	400–720	8	✗	✗	✗	✗
Chakrabarti [21]	$1392 \times 1040$	31	420–720	50	✗	✗	✗	✗
BGU [22]	$1392 \times 1300$	244	400–1000	201	✗	✗	✗	✗
Mian [23]	$752 \times 480$	33	400–720	31	✓	✗	✗	✗
Proposed	$1600 \times 1200$	31	400–700	1890	✓	✓	✓	✓

	NOCS	TNOCS
Family house	30.83 dB	36.43 dB
Medieval seaport	40.06 dB	44.87 dB
City	43.61 dB	45.39 dB
Outdoor	28.94 dB	34.05 dB
Forest	27.03 dB	30.67 dB
Indoor	39.33 dB	41.15 dB
Lab	35.40 dB	36.26 dB
Average	35.03 dB	38.40 dB

	PRIBP		HEVC Inter-Coding
	BD Rate	BD PSNR	BD Rate	BD PSNR
Fam. house	−10.41%	0.59 dB	−54.55%	4.02 dB
M. seaport	−7.15%	0.35 dB	−22.82%	1.16 dB
City	−9.29%	0.44 dB	−19.36%	0.92 dB
Outdoor	−9.22%	0.47 dB	−65.81%	4.82 dB
Forest	13.24%	−0.59 dB	−73.49%	6.09 dB
Indoor	−6.77%	0.22 dB	12.71%	−0.38 dB
Lab	−9.21%	0.57 dB	42.61%	−1.85 dB
Average	−3.36%	0.15 dB	−52.46%	3.27 dB

Database	Spatial Res.	# Channels	Wavel. Area (nm)	# Images	Videos	Rerendering	Cam. Array	Depth
CAVE [16]	$512 \times 512$	31	400–700	32	✗	✗	✗	✗
UGR [17]	$1000 \times 900$	61	400–1000	14	✗	✗	✗	✗
Hordley [18]	$\sim 336 \times 271$	31	400–700	23	✗	✗	✗	✗
Le Moan [19]	$500 \times 500$	160	410–1000	9	✗	✗	✗	✗
Foster [20]	$1018 \times 1339$	33	400–720	8	✗	✗	✗	✗
Chakrabarti [21]	$1392 \times 1040$	31	420–720	50	✗	✗	✗	✗
BGU [22]	$1392 \times 1300$	244	400–1000	201	✗	✗	✗	✗
Mian [23]	$752 \times 480$	33	400–720	31	✓	✗	✗	✗
Proposed	$1600 \times 1200$	31	400–700	1890	✓	✓	✓	✓

	NOCS	TNOCS
Family house	30.83 dB	36.43 dB
Medieval seaport	40.06 dB	44.87 dB
City	43.61 dB	45.39 dB
Outdoor	28.94 dB	34.05 dB
Forest	27.03 dB	30.67 dB
Indoor	39.33 dB	41.15 dB
Lab	35.40 dB	36.26 dB
Average	35.03 dB	38.40 dB

Abstract

Supplementary Material (1)

Data availability

Cited By

Figures (11)

Tables (3)

Equations (23)

Journal of the Optical Society of America A