Linde, Buzo, and Gray (LBG) proposed a 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 their research paper, "Design of a Vector Quantization Image Codebook Using a Training Sequence," Linde, Buzo, and Gray (LBG) proposed a VQ design algorithm that relies on a training sequence. This training sequence is incredibly useful as it eliminates the need for multi-dimensional integration, which can be a time-consuming process. The LBG algorithm is an iterative process, meaning that it involves multiple iterations. During each iteration, a large set of vectors, typically referred to as a training set, needs to be processed. This set is constructed using vectors sampled from a group of typical signals that will be encoded altogether. The training set, denoted as T={x1,x 2,?.x M}, contains xi, which represents a sampled training vector, and M, which represents the size of the training set. It is important to note that the size of the training set is far greater than the codebook size N. By increasing the size of the training set, the quality of the encoding can be improved, leading to better compressed images with fewer artifacts and distortions.