Dictionary learning static image lossy compression method based on minimum quantization error criterion

Abstract

The invention discloses a dictionary learning static image lossy compression method based on minimum quantization error criterion, and is to solve the problem of large quantification error of an existing static image lossy compression method. The technical scheme is characterized by adding information entropy of an index corresponding to each sparse coefficient into a sparse coding objective function as a regularization term; when selecting dictionary atoms through an orthogonal matching pursuit algorithm, minimizing the information entropy to limit dispersity of the dictionary atoms, thereby reducing coding cost of the index corresponding to each sparse coefficient; and meanwhile, in the dictionary learning process, ranking the sparse coefficients and finding k habit sequence division capable of enabling the total dispersion square sum of the sparse coefficients to be minimum, each division serving as one quantification group, different quantification groups adopting different quantization steps, the same quantification group adopting the same quantization steps, thereby enabling the final quantization error to be minimum.

Description

technical field [0001] The invention relates to a static image lossy compression method, in particular to a dictionary learning static image lossy compression method based on the minimum quantization error criterion. Background technique [0002] The document "Compressibility constrained sparse representation with learned dictionary for low bit-rate image compression, IEEE Transactions on Circuits and Systems for Video Technology, 2014, Vol24(10), p1743–1757" discloses a sparse coding method based on convex relaxation and compression constraints Lossy compression for images. This method replaces the traditional tracking matching algorithm with convex relaxation-based sparse coding, which strengthens the sparsity and stability of image representation coefficients. At the same time, the compression constraint is added to the solution process of sparse coding, and the sparse coding problem is transformed into The norm optimization problem approaches the optimal solution of t...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the shortcomings of the existing static image lossy compression methods with large quantization errors, the present invention provides a dictionary learning static image lossy compression method based on the minimum quantization error criterion

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