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

This addresses data storage and transmission challenges for medical researchers and clinicians, but it is incremental as it compares existing methods.

This paper tackled the problem of compressing high-dimensional neuroimaging data by comparing Tucker decomposition and Singular Value Decomposition (SVD), finding that Tucker decomposition achieves superior reconstruction fidelity and perceptual similarity while SVD excels in extreme compression but sacrifices fidelity.