Linear Algebraic Approaches to Neuroimaging Data Compression: A Comparative Analysis of Matrix and Tensor Decomposition Methods for High-Dimensional Medical Images

2025/11/22 by Kim, Jaeho, David, Daniel, Vizitiv, Ana
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2511.18197

Abstract

This paper evaluates Tucker decomposition and Singular Value Decomposition (SVD) for compressing neuroimaging data. Tucker decomposition preserves multi-dimensional relationships, achieving superior reconstruction fidelity and perceptual similarity. SVD excels in extreme compression but sacrifices fidelity. The results highlight Tucker decomposition's suitability for applications requiring the preservation of structural and temporal relationships.

Citations

• Tensor Regression with Applications in Neuroimaging Data Analysis
• Tensor Decompositions and Applications
• The Approximation of One Matrix by Another of Lower Rank

Related

• SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data
• Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition
• Novel Factorization Strategies for Higher Order Tensors: Implications for Compression and Recovery of Multi-linear Data
• Tucker Tensor Regression and Neuroimaging Analysis
• Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encoding