Research On Distributed Video Compressed Coding Based On Quantum Particle Swarm Optimization

Compressed sensing (CS) is a new sampling method to capture and encode signals. It breaks through the Nyquist sampling theorem restrictions sparse signal sampling theory. Conventional signal compression method compress the huge anount of data after the analog signal is digitized. But in the theory of compressed sensing, sampling and compression of the image signal simultaneously proceed. It greatly reduced the computational complexity and developed the signal acquisition technology. This paper focuses on the reconstruction algorithm of compressed sensing, proposes a compressed sensing reconstruction algorithm based on quantum particle swarm optimization for image signal. And then, this paper uses the proposed algorithm as the reconstructed algorithm of distributed compressed video sensor to deal with video signal.This paper focuses on the following three aspects:First, this paper researches the theory of compressed sensing and the theory of distributed compressed sensing. This paper introduces the main context of the theory of compressed sensing, it inclues signal sparse measurement, matrix construction and reconstruction algorithm. And this paper introduces the theory of distributed compressed sensing. This paper also researches the technology of distributed compressed video sensing based on the three joint sparse models.Second, this papper proposes a reconstruction algorithm of compressed sensing based on quantum particle swarm optimization. The results show that, compared with the orthogonal matching pursuit algorithm, the compressed sensing reconstruction algorithm based on quantum particle swarm optimization can acquire the better reconstructed image when the compression ratio is smaller. And it has the lower complexity and fast convergence ability for image processing.Third, the proposed algorithm is applied on distributed compressed video sensor. For the video signal as the object of study, simulation results demonstrate that, compared with the orthogonal matching pursuit algorithm, the compressed sensing reconstruction algorithm based on quantum particle swarm optimization improves the quality of the recovered video with low complexity. And it makes the reconstruction algorithm more stable.