Structural Sparse-Based Scalable Compressed Video Tensor Signal Acquisition and Reconstruction System

Abstract

The present invention provides a scalable and compressed video tensor signal acquisition and reconstruction system based on structured sparseness, including: a tensor base construction module, a tensor decomposition module, a scalable sensing module and a scalable reconstruction module, wherein the The tensor basis construction module uses the data-driven tensor quantum space joint model and layered tensor quantum space learning to generate sparse basis matrices for key frame blocks of video signals. The scalable compressed tensor sampling provided by the present invention conforms to the distributed progressive structure of the video sampling process, and the progressive construction of the structured sparse base matrix also improves the accuracy and efficiency of reconstruction, thereby improving the reliability of the video signal. Scaling sampling efficiency, achieving reconstruction gain compared with other methods under different sampling compression ratios, and also has good scalability.

Description

technical field [0001] The invention relates to the technical field of video signal processing, in particular to a scalable and sparse-based video tensor signal acquisition and reconstruction system. Background technique [0002] In the era of intelligent information, various devices are connected through heterogeneous networks for wired or wireless transmission of information. In a heterogeneous network, the fluctuation of network bandwidth conditions, and the information processing capabilities and application scenarios of various hardware devices are different, because the video code stream of the encoding and decoding technology (especially video encoding technology) for high-dimensional multimedia signals is fixed. , so there is not enough flexibility for different needs. [0003] In the past 20 years, various Scalable Video Coding (SVC) technologies have been proposed and standardized to reduce coding complexity, fill the gap of scalable implementation solutions in ac...

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Problems solved by technology

However, the sparse base of this method is a fixed base matrix, so it cannot use structural information to provide more effective sparsity and adaptability

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