Quantum Block-Matching Algorithm using Dissimilarity Measure
This work addresses image processing tasks such as video compression and noise reduction, but it is incremental as it applies quantum methods to an existing block-matching approach.
The authors tackled the problem of finding similar image blocks for applications like video compression and noise reduction by proposing a quantum block-matching algorithm using a dissimilarity measure based on the quantum Fourier transform or Swap test. They conducted experiments with small cases on ideal and noisy simulations, including tests on IBM and IonQ quantum devices, showing potential for near-term applications.
Finding groups of similar image blocks within an ample search area is often necessary in different applications, such as video compression, image clustering, vector quantization, and nonlocal noise reduction. A block-matching algorithm that uses a dissimilarity measure can be applied in such scenarios. In this work, a measure that utilizes the quantum Fourier transform or the Swap test based on the Euclidean distance is proposed. Experiments on small cases with ideal and noisy simulations are implemented. In the case of the Swap test, the IBM and IonQ quantum devices have been used, demonstrating potential for future near-term applications.