Scalable Compression Video Acquisition and Reconstruction System Based on Structured Sparse

Abstract

The present invention provides a scalable and compressed video acquisition and reconstruction system based on structured sparseness, including: a structured sparse learning module, a signal decomposition module, a scalable sensing module and a scalable reconstruction module, wherein the structure The sparse learning module utilizes the data-driven subspace joint model and hierarchical subspace learning to generate sparse basis matrices at the encoding and decoding ends. The scalable and compressed 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, and improves the scalable sampling efficiency of video signals , which achieves 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 multimedia signal processing, in particular to a scalable and compressed video acquisition and reconstruction system based on structured sparse. 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, as well as the different information processing capabilities and application scenarios of various hardware devices, due to the fixed video stream of high-dimensional multimedia signal codec technology (especially video coding technology) , so there is not enough flexibility for different needs. [0003] In the past 20 years, various Scalable Video Coding (Scalable Video Coding, SVC) technologies have been proposed and standardized to reduce the coding complexity, fill the vacancy of scalable implementation so...

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Problems solved by technology

After searching the literature of the prior art, it was found that Stankovic et al. proposed a scalable compression video sampling method in the article "Scalable compressive Video" published at the "IEEE IEEE Int.Conf.ImageProcess." (IEEE ICIP) conference in 2011 , this method provides a quality scalable coding scheme for compressed video samples, but the sparse base of this method is a fixed base matrix, which cannot provide more effective sparsity and adaptability

Subsequently, B.Bojana et al. proposed to use KSVD to adaptively learn sparse patterns in the paper "Learning scalable dictionaries with application to scalable compressive sensing" published at the "IEEE Proc.European Signal Process.Conf" (IEEE EUSIPCO) conference in 2012. To support the compressed video sampling scheme with scalable quality, however, because it is based on a general sparse representation, it cannot effectively describe the non-stationary statistical properties of high-dimensional signals

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