VQ design algorithm based on a training sequence

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Linde， Buzo， and Gray (LBG) proposed a VQ design algorithm based on a training sequence. The use of a training sequence bypasses the need for multi-dimensional integration. The LBG algorithm is of iterative type and in each iteration a large set of vector

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In the context of the document: Linde, Buzo, and Gray (LBG) proposed a VQ design algorithm based on a training sequence. This algorithm offers a solution that eliminates the need for multi-dimensional integration by utilizing a training sequence. The LBG algorithm is of the iterative type, meaning that it goes through multiple iterations to achieve its goal. In each iteration, a large set of vectors, known as the training set, is processed. Typically, this training set, denoted as T={x1, x2, ..., xM}, consists of sampled vectors from a group of typical signals that will be encoded together. Here, xi represents a sampled training vector, and M represents the size of the training set, which is significantly larger than the codebook size N.