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.

翻译：在视频压缩、图像聚类、向量量化以及非局部降噪等不同应用中，通常需要在足够大的搜索区域内查找相似的图像块组。采用相异度度量的块匹配算法可应用于此类场景。本文提出了一种基于欧氏距离、利用量子傅里叶变换或交换测试的度量方法。我们在理想仿真和噪声仿真条件下对小规模案例进行了实验。在交换测试案例中，已使用IBM和IonQ量子设备进行验证，显示了未来近期应用的潜力。