Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
It provides a practical framework for efficient compression and reconstruction of biological volumetric data, offering a trade-off between quality and speed for researchers in bioimaging.
This work introduces Structured 3D-SVD for compressing and reconstructing biological volumetric images, achieving reconstruction quality close to Tucker decomposition with shorter computation times and outperforming CPD in both accuracy and runtime on fish and brain datasets.
This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain and supports progressive reconstruction through ordered quasi-singular coeffients. The experimental evaluation was carried out on two biological volumetric datasets: one full-volume scan of a fish and another of a brain. The results show that Structured 3D-SVD achieves reconstruction quality close to that of Tucker decomposition while requiring shorter computation times and outperforms canonical polyadic decomposition (CPD) in both accuracy and runtime. In addition, a progressive reconstruction analysis shows that relatively low truncation levels are sufficient to preserve the main volumetric structures, while higher truncation levels lead to more detailed reconstructions.