A Storage Optimization Method for Sparse Coding

Abstract

The invention discloses a storage optimization method for sparse coding, which uses the mean square error as the objective function to perform sparse representation on the target signal y, and calculates the linear coefficient s of the sparse representation result i ; On the basis of measuring the consistency of different images, analyze the PSNR value and contrast of the reconstructed image based on the mean square error and the reconstructed image based on the structural similarity; according to the analysis results, the linear coefficient s in the sparse representation results i The bits are allocated, and the coding result of the sparse coding is stored using the minimum number of bits, so as to realize the storage optimization of the sparse coding. The sparse coding storage optimization method of the present invention is based on in-depth analysis and research on different image measurement methods, and analyzes the bit allocation problem of sparse coding by comprehensively considering the PSNR value and image contrast of the reconstructed image, and according to the analysis results, completes The optimal bit allocation method of sparse coding is realized, that is, the storage optimization of sparse coding is realized, and the storage space of sparse coding is saved.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a sparse coding storage optimization method. Background technique [0002] Signal representation and encoding has always been a research hotspot in the field of information, and it is also one of the fastest-growing directions in information technology. The digitization of analog signals enables information in the real world to be collected and processed by computers. With the rapid development of storage technology, the storage capacity of hardware devices is also increasing, and various types of information are also increasing at a faster rate. Generally speaking, the use of point sets in low-dimensional space to represent signals has the advantages of rich information and strong intuition. But at the same time, it also contains a lot of redundant information, which not only takes up more network resources, but also is not conducive to the analysis and processing of ...

AI Technical Summary

Problems solved by technology

In the sparse coding storage of signals, the quality of the sparse coding storage mode not only affects the memory space occupied when the signal is saved, but also the data storage accuracy of the sparse coefficients of the sparse coding will also have a great impact on the quality of the reconstructed signal. At present, the storage of sparse coding is not optimized enough, and it takes up a lot of storage space

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