Image compression and entanglement

TL;DR

This paper introduces a novel image compression method based on quantum-inspired matrix product state representations, linking quantum entanglement to classical image correlations for efficient compression.

Contribution

It proposes a new approach that uses quantum entanglement concepts to achieve image compression through MPS truncation and additional Fourier and entropy-based techniques.

Findings

Effective image compression via MPS truncation.

Enhanced compression with Fourier preprocessing.

Improved lossless compression through entropy methods.

Abstract

The pixel values of an image can be casted into a real ket of a Hilbert space using an appropriate block structured addressing. The resulting state can then be rewritten in terms of its matrix product state representation in such a way that quantum entanglement corresponds to classical correlations between different coarse-grained textures. A truncation of the MPS representation is tantamount to a compression of the original image. The resulting algorithm can be improved adding a discrete Fourier transform preprocessing and a further entropic lossless compression.