Compressed video tensor signal collection and reconstruction system and method

Abstract

The invention provides a compressed video tensor signal collection and reconstruction system and method. The system comprises a structured sparse tensor dictionary learning module, a tensor sensing module and a reconstruction processing module, wherein the structured sparse tensor dictionary learning module firstly obtains a training set by using a subspace clustering method; a tensor subspace learning method and a block sparse tensor dictionary learning method based on block correlation minimization are utilized to obtain a dictionary, a tensor sensing module projects a video tensor signal inthe form of an image tensor block, and obtained data is finally decoded and reconstructed in a reconstruction processing module. According to the invention, while compressed sampling is provided, a distributed progressive structure of a video sampling process is also conformed; the reconstruction accuracy and efficiency are also improved for the special structure of the structured sparse dictionary matrix, the sampling efficiency of video signals is improved, reconstruction gain is obtained under different sampling compression ratios compared with other methods, and meanwhile good expandability is achieved.

Description

Technical field [0001] The invention relates to the technical field of video signal processing, in particular to a system and method for acquiring and reconstructing compressed video tensor signals. 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 collection and coding (compression) of video signals is very important for applications such as video storage and transmission. Under this traditional framework, compression coding schemes for high-dimensional signals, especially video signals, have been developing. However, the core problem of information redundancy caused by the bloated traditional framework has not been fundamentally solved. 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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