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.