A quantum moving target segmentation algorithm for grayscale video

TL;DR

This paper introduces a quantum algorithm for real-time moving target segmentation in grayscale videos, leveraging quantum parallelism to achieve exponential speedup over classical methods.

Contribution

It proposes a novel quantum algorithm with detailed circuit design for efficient moving target segmentation, surpassing existing quantum and classical approaches in speed.

Findings

Achieves exponential speedup over classical algorithms.

Demonstrates feasibility on IBM Q quantum hardware.

Reduces complexity to O(n^2 + q) for quantum videos.

Abstract

The moving target segmentation (MTS) aims to segment out moving targets in the video, however, the classical algorithm faces the huge challenge of real-time processing in the current video era. Some scholars have successfully demonstrated the quantum advantages in some video processing tasks, but not concerning moving target segmentation. In this paper, a quantum moving target segmentation algorithm for grayscale video is proposed, which can use quantum mechanism to simultaneously calculate the difference of all pixels in all adjacent frames and then quickly segment out the moving target. In addition, a feasible quantum comparator is designed to distinguish the grayscale values with the threshold. Then several quantum circuit units, including three-frame difference, binarization and AND operation, are designed in detail, and then are combined together to construct the complete quantum circuits for segmenting the moving target. For a quantum video with $2^m$ frames (every frame is a $2^n\times 2^n$ image with $q$ grayscale levels), the complexity of our algorithm can be reduced to O$(n^2 + q)$. Compared with the classic counterpart, it is an exponential speedup, while its complexity is also superior to the existing quantum algorithms. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.