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

A vector quantization and image compression technology, applied in image communication, television, electrical components, etc., can solve problems such as inability to guarantee the global optimal codebook, low search efficiency, and falling into local optimal values

Inactive Publication Date: 2010-05-19
SHENZHEN UNIV
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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 prob...

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

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[0078] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0079] The technical problem to be solved by the present invention is to propose an improved image vector quantization codebook optimization design method applied to image compression-neighborhood particle-pair optimizer (NPPO). This method can more effectively prevent particles from falling into the local optimal codebook, and make the overall codebook closer to the global optimal solution. Improved search efficiency.

[0080] The NPPO algorithm follows the idea of ​​using two particles in the PPO algorithm to form a particle pair with a smaller population size, and still adopts a cooperative working relationship, such as Figure 5 shown. First, two particles {IP 1 , IP 2}, forming the initial particle pair. Each particle calls ...

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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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Application Information

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IPC IPC(8): H04N7/26H04N19/124H04N19/176H04N19/94
Inventor 纪震储颖周家锐
Owner SHENZHEN UNIV
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