A compressed video tensor signal acquisition and reconstruction system and method

Abstract

The invention provides a compressed video tensor signal acquisition and reconstruction system and method, including: a structured sparse tensor dictionary learning module, a tensor sensing module and a reconstruction processing module, wherein: structured sparse tensor dictionary learning The module first uses the subspace clustering method to obtain the training set, and then uses the tensor quantum space learning method and the block sparse tensor dictionary learning method based on block correlation minimization to obtain the dictionary. The projection is performed in the form of gauge blocks, and the obtained data is finally decoded and reconstructed in the reconstruction processing module. The present invention provides compressed sampling while also conforming to the distributed progressive structure of the video sampling process, and the special structure of the structured sparse dictionary matrix also improves the accuracy and efficiency of reconstruction, and improves the sampling efficiency of video signals. Compared with other methods, the reconstruction gain is obtained under different sampling compression ratios, and it also has good scalability.

Description

technical field [0001] The invention relates to the technical field of video signal processing, in particular to a compressed video tensor signal acquisition and reconstruction system and method. Background technique [0002] As the main carrier of the intelligent information age, high-dimensional multimedia signals such as images and videos provide the main information content for people's work and life, and occupy an increasing proportion. The acquisition and encoding (compression) of video signals is crucial for applications such as storage and transmission of video. Under this traditional framework, compression coding schemes for high-dimensional signals, especially video signals, have been developed. However, the core problem of information redundancy caused by the bloated traditional framework has not been fundamentally resolved. To solve this problem, it is necessary to break through the limitation of sampling first and then compressing in the traditional framework....

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Problems solved by technology

[0003] After searching the literature of the prior art, it was found that S. Friedland and Q. Li et al. proposed a single-sheet quantum Space signal sampling theory, which gives the conditions of uniqueness and stability for tensor signal sampling in a single quantum space, but the subspace set assumed by the theory is formed by a fixed basis , can not provide more effective sparsity and adaptability

Y.Li and H.Xiong proposed the application of compressive sensing to From video sampling, this method directly compresses and samples the video tensor signal at the sampling encoding end, and uses the UoTS base as the sparse base to reconstruct the tensor signal at the decoding end. This method can flexibly and effectively sparse the tensor signal representation to ensure the subjective quality of the video obtained by reconstruction, but the UoTS base used in this method does not consider the overlap between the subspaces of each tensor, which is manifested in the fact that the correlation between blocks is so high that it is impossible to obtain a compact block sparse , which in turn reduces the effect of

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