Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video Coding

TL;DR

This paper introduces a synthetic hyperspectral video database with depth maps, enabling evaluation of algorithms for cross-spectral reconstruction and hyperspectral video coding, demonstrating significant improvements in PSNR and compression rates.

Contribution

The paper presents a novel synthetic hyperspectral video database with depth information and two new algorithms for cross-spectral reconstruction and video coding, showcasing their effectiveness.

Findings

PSNR increased by up to 5.6 dB in reconstruction.

Rate savings of up to 10% in hyperspectral video coding.

Database and source code publicly available.

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 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. The novel hyperspectral video database and source code is available at https:// github.com/ FAU-LMS/ HyViD for use by the research community.