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In our treatment of the Vector Quantization subject, we studied the problem of Colored Image Compression by Vector Quantization. Vector Quantization, in general, is an optimization process that is used for vectors classification. For a given group of vectors, and a given number, Vector Quantization will try to segment this group into different subgroups, in a way that minimizes the distance of each vector from its group center. This technique is very useful for compression, especially for natural signals, where many parts of the signal are nearly the same. In the case of an image, Vector Quantization is applied, first, by segmenting the signal into blocks -usually squares of a given size, and next, by considering each block as a vector, which is finally submitted to the Quantization process. For colored image compression, we’ve adapted Gray Image Compression by Vector Quantization Algorithm to the colored case. The results, are very promising and more advanced in the compression quality and ratio than those presented by other sources dealing with this subject. The enhancement that is presented by the results, cover also the domain of gray image compression. While we used the original K-means Algorithm in our development, we‘ve succeeded in realizing these enhancements by modifying the K-means algorithm so that attention is given to delicate points important to the general performance of the algorithm. Also an important correction to the way in which the compression ratio is calculated was presented.