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Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a Signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined Linear systems. This is based on the principle that, through Optimization, the sparsity of a signal can be exploited to recover it from far fewer Samples than required by the Nyquist-Shannon sampling theorem. There are two conditions under which recovery is possible:

• Sparsity which requires the signal to be sparse in some domain.
• Incoherence which is applied through the isometric property which is sufficient for sparse signals.

See here for definition

Basically, as in Data compression, it takes advantage of the order, or Randomness deficit in the data to sample below the Nyquist-Shannon limit

Compressed sampling

Introduction to compressed sensing

https://www.wikiwand.com/en/Compressed_sensing