Neighborhood particle pair optimization method applied to image vector quantization of image compression

Abstract

The invention relates to a neighborhood particle pair optimization method applied to image vector quantization of image compression. The method comprises that: code words are randomly selected from training vectors to form initial codebooks; each codebook is represented by one particle; two particles are randomly selected to form an initial particle pair; each particle is subjected to speed update and position update through a weighting PSO algorithm and is subjected to clustering operation through a K-means algorithm in each iteration; particle pair iterations of which iterative algebra is genmax are performed in total; in a jth particle pair iteration, the winner particle is named a jth-generation elite particle; a certain vector is randomly selected in a neighborhood of the jth-generation elite particle as a neighborhood particle to form a jth-generation neighborhood particle pair together with the jth-generation elite particle; and when j is equal to genmax, the elite particle is a genmax-generation elite particle which is a solution of the neighborhood particle pair optimization method. The method has the advantages of reducing the influence of initial codebook distribution on optimization results and significantly improving the quality of reconstructed images.

Description

technical field [0001] The invention relates to the technical field of image compression, in particular to a neighborhood particle pair optimization method for image vector quantization applied to image compression. Background technique [0002] Vector Quantization (Vector Quantization, VQ) is to use fewer codewords to represent and replace a large number of vectors, so as to achieve the purpose of compression. Its mathematical description is, let x be M L-dimensional training vector sets, namely ∀ i = 1,2 , . . . , M , in is an L-dimensional Euclidean space. Y is a codebook composed of N L-dimensional codewords, that is, Y={y 1 ,y 2 ,...,y j ,...,y N}, ∀ j = 1,2 , . . . , N . Vector quantization is to assign M training vectors to N clusters, and each c...

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Problems solved by technology

[0048] The technical problem to be solved by the present invention is to propose a neighborhood particle pair optimization method for image vector quantization applied to image compression, so as to solve the problem of low search efficiency in the prior art and the selection of the initial codebook It has a certain sensitivity and may fall into a local optimum, and cannot guarantee that the global optimal codebook can be found

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