LBG算法是一种经典的向量量化和图像压缩技术,通过迭代过程将输入数据分割成多个代表性的码书向量,有效减少数据存储需求同时保持良好的视觉质量。
Linde, Buzo, and Gray (LBG) proposed a vector quantization (VQ) design algorithm that relies on a training sequence. This approach eliminates the need for multidimensional integration. The LBG algorithm is iterative in nature; in each iteration, it requires processing a large set of vectors known as the training set. Typically, this training set T={x1,x2,...,xM} consists of vectors sampled from a collection of typical signals to be encoded together. Here, xi represents an individual sampled vector and M denotes the size of the training set, which is significantly larger than the codebook size N.