Video signal collection and reconfiguration system based on high-dimension compressed sensing

Abstract

The invention provides a video signal collection and reconfiguration system based on high-dimension compressed sensing. The video signal collection and reconfiguration system comprises a complex sensing matrix construction and optimization module, a sparse base matrix construction module, a video signal universe sensing module and a reconfiguration processing module. An optimized complex sensing matrix and a high-dimension sparse base are respectively generated from the complex sensing matrix construction and optimization module and the sparse base matrix construction module with matrix operations of Kronecker products. Projection of video signals on the matrix is generated for the optimized complex sensing matrix by the universe sensing module. The acquired data are finally decoded and reconfigured in the reconfiguration processing module. While synchronous compressing and sampling in time-space domain are provided, distributed progressive structure during video sampling is adopted; accuracy and efficiency of reconfiguration are improved by corresponding optimization of the sensing matrix; sampling efficiency of the video signal is greatly improved; 4 dB sampling gain is acquired as the maximum value with varied sampling compressing rate; and the video signal collection and reconfiguration system has high scalability.

Description

technical field [0001] The invention relates to a video signal acquisition scheme, in particular to a video signal acquisition and reconstruction system based on high-dimensional compressed sensing. Background technique [0002] The capture and encoding (compression) of video signals is critical for applications such as storage and transmission of video. The traditional signal processing system adopts the mode of sampling first and then compressing: in order to completely preserve all information of the signal, the video should be sampled at a sampling frequency not less than twice the signal bandwidth; the collected original signal is removed after a series of encoding techniques For the purpose of redundancy, the bottleneck of related technologies is that a large amount of sensors and computing resources are spent to obtain a small amount of signal compression data after processing, and the demand for resources at the sampling end is too high. In order to further improve ...

AI Technical Summary

Problems solved by technology

This method can effectively improve the efficiency of video sampling and ensure the subjective quality of the reconstructed video, but this method ignores the similarity of the signal in the time dimension of the video sequence, so that the sampled data still contains redundant information , so that the high-dimensional features of the video signal cannot be fully utilized, in fact, the sensing matrix It can be seen that this scheme can be attributed to the compressed sensing based on reconstruction optimization

In addition, since the selected Φ is a random matrix, it is impossible to provide 1 Optimal sensing for convex relaxation reconstruction algorithms, thus loss of reconstruction accuracy at the computational power of the projection

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