modescompression

Modescompression is a data compression approach that uses modal decomposition to represent a signal or dataset as a weighted sum of a small number of basis modes. By extracting a set of orthogonal or physically meaningful modes that capture the dominant structure of the data, the method stores only the coefficients for the chosen modes and the mode definitions, enabling compact representation with reconstruction at a reduced resolution.

The typical workflow starts with computing a modal basis using techniques such as proper orthogonal decomposition (POD), singular value decomposition (SVD), dynamic mode decomposition (DMD), Fourier, or wavelet transforms. A subset of modes is then selected according to criteria such as energy capture, information criteria, or a user-defined tolerance for reconstruction error. The amplitudes or coefficients associated with the selected modes are quantized and encoded, often with entropy coding, to achieve further compression.

Applications of modescompression span engineering and science domains that produce high-dimensional, structured data. It is used in structural dynamics and aeroacoustics to compress sensor data, in computational fluid dynamics and seismology to reduce large simulation outputs, and in meteorology or environmental sensing where coherent spatial-temporal patterns dominate the signal.

Advantages include efficient compression for data with low-rank modal structure and the preservation of interpretable features tied to physical processes. Limitations involve dependence on representative data to form a meaningful basis, sensitivity to noise, potential loss of fine details when too few modes are kept, and computational cost of the initial decomposition. Modescompression often sits alongside other dimensionality reduction and compression methods as a way to balance fidelity and storage.