Compressed video acquisition and reconstruction system based on data driven subspace set

Abstract

The invention provides a compressed video acquisition and reconstruction system based on a data driven subspace set. The compressed video acquisition and reconstruction system comprises a subspace set construction module, a sparse basis matrix construction module, a video signal sensing module and a reconstruction processing module, wherein a clustering method is utilized by the subspace set construction module to generate the subspace set, a linear subspace learning method is utilized by the sparse basis matrix construction module to generate a sparse basis corresponding to the subspace set, video signals are projected in a block mode through the sensing module, and obtained data are decoded and reconstructed in the reconstruction processing module at last. Compressed sampling is provided, meanwhile a distributed gradual model structure in a video sampling process agrees with the system, and reconstruction accuracy and efficiency of a sparse basis matrix special structure is also promoted. Sampling efficiency of video signals is greatly improved, reconstruction gains are obtain compared with other methods under different sampling compression ratios, and meanwhile the system has good expandability.

Description

technical field [0001] The invention relates to a video signal acquisition scheme, in particular to a compressed video acquisition and reconstruction system based on a data-driven subspace set. 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 the a...

AI Technical Summary

Problems solved by technology

Yue M.Lu and Minh N.Do proposed signal sampling based on subspace sets in the article "A Theory for Sampling Signals From a Union of Subspaces" published in the journal "IEEE Transactions on Signal Processing" (TSP) in 2008. Theory, the theory gives the uniqueness and stability conditions to be satisfied for the sampling of the signal in the subspace set, but the subspace set assumed by the theory is formed by a fixed basis, which cannot provide a more effective Sparsity and Adaptability

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