Image compression and entanglement

By José I. Latorre. arXiv.org, 2005

337 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1af4d977c3267b9e1db6029d509304fb6915ed09

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Citations in the corpus

NEQR: a novel enhanced quantum representation of digital images (2013, methodology): “To date, four quantum image-representation models have been proposed to store and process images, including Qubit Lattice, Entangled Image, Real Ket, and flexible representation of quantum images (FRQI).”
Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train (2016, methodology): “The concept of KA was originally introduced in [45] for casting a grayscale image into real ket state of a Hilbert space, which is simply a higher-order tensor, using an appropriate block structured addressing.”
A survey of quantum image representations (2015, methodology): “Following that, entangled images, in which geometric shapes are encoded in quantum states [95]; and Real Ket, where the images are quantum states having gray levels as coefficients of the states [49], were proposed by Venegas-”