Linde, Buzo, and Gray (LBG) Proposed a Training Sequence-Based VQ Design Algorithm

In their seminal paper "Design of a Vector Quantization Image Codebook Using a Training Sequence," Linde, Buzo, and Gray (LBG) presented a VQ design algorithm grounded in training sequences. This approach bypasses computationally intensive multi-dimensional integration by leveraging sampled data. The LBG algorithm follows an iterative refinement process (often implemented using Lloyd's algorithm), where each iteration processes a substantial vector set—known as the training set—constructed from typical signal samples. Denoted as T = {x₁, x₂, ..., xₘ}, the training set contains M sampled vectors xi, with M >> N (codebook size). Increasing M enhances encode-decode fidelity by improving centroid calculations during clustering, ultimately yielding higher-quality compressed images with reduced artifacts. In practice, implementations initialize codebooks via splitting techniques and optimize partitions using distortion metrics like Mean Squared Error.