Sparse coding storage optimization method

Abstract

The invention discloses a sparse coding storage optimization method, which comprises the following steps of: carrying out sparse representation on a target signal y by taking a mean square error as an objective function, and calculating a linear coefficient si of a sparse representation result; analyzing PSNR values and contrast ratios of the reconstructed image based on the mean square error and the reconstructed image based on the structural similarity on the basis of the consistency of different image metrics; and allocating bits of the linear coefficient si in the sparse representation result according to the analysis result, and storing the coding result of the sparse coding by using the least bit number to realize storage optimization of the sparse coding. According to the sparse coding storage optimization method, based on deep analysis and research on different image measurement methods, the bit distribution problem of sparse coding is analyzed by comprehensively considering the PSNR value of the reconstructed image and the image contrast, the optimal bit distribution method of sparse coding is completed according to the analysis result, namely, storage optimization of sparse coding is achieved, 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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