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

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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.

© 2023 Optica Publishing Group

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Supplementary Material (1)

NameDescription
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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