  The VVC codec is applied to the task of multispectral image (MSI) compression
using adaptive and scalable coding structures. In a 'plain' VVC approach,
concepts from picture-to-picture temporal prediction are employed for
decorrelation along the MSI's spectral dimension. The popular principle
component analysis (PCA) for spectral decorrelation is further evaluated in
combination with VVC intra-coding for spatial decorrelation. This approach is
referred to as PCA-VVC. A novel adaptive MSI compression algorithm, named
HPCLS, is introduced, that uses PCA and inter-prediction for spectral and VVC
intra-coding for spatial decorrelation. Further, a novel adaptive scalable
approach is proposed, that provides a separately decodable spectrally scaled
preview of the MSI in the compressed file. Information contained in the preview
is exploited in order to reduce the overall file size. All schemes are
evaluated on images from the ARAD HS data set containing outdoor scenes with a
high variety in brightness and color. We found that 'Plain' VVC is outperformed
by both PCA-VVC and HPCLS. HPCLS shows advantageous rate-distortion (RD)
behavior compared to PCA-VVC for reconstruction quality above 51dB PSNR. The
performance of the scalable approach is compared to the combination of an
independent RGB preview and one of HPCLS or PCA-VVC. The scalable approach
shows significant benefit especially at higher preview qualities.
