This article is missing information about general information, usage in the field (mention cinepak?), optimality conditions, choice of 𝜖s, model instead of training data, ELBG. Please expand the article to include this information. Further details may exist on the talk page.(December 2023)
This article relies largely or entirely on a single source. Relevant discussion may be found on the talk page. Please help improve this article by introducing citations to additional sources. Find sources: "Linde–Buzo–Gray algorithm" – news · newspapers · books · scholar · JSTOR(December 2023)
The Linde–Buzo–Gray algorithm (named after its creators Yoseph Linde, Andrés Buzo and Robert M. Gray, who designed it in 1980)[1] is an iterativevector quantization algorithm to improve a small set of vectors (codebook) to represent a larger set of vectors (training set), such that it will be locally optimal. It combines Lloyd's Algorithm with a splitting technique in which larger codebooks are built from smaller codebooks by splitting each code vector in two. The core idea of the algorithm is that by splitting the codebook such that all code vectors from the previous codebook are present, the new codebook must be as good as the previous one or better. [2]: 361–362
Descriptionedit
The Linde–Buzo–Gray algorithm may be implemented as follows:
algorithm linde-buzo-gray isinput: set of training vectors training, codebook to improve old-codebookoutput: codebook that is twice the size and better or as good as old-codebooknew-codebook ← {} for eachold-codevectorinold-codebookdo insert old-codevector into new-codebook insert old-codevector + 𝜖 into new-codebook where 𝜖 is a small vector return lloyd(new-codebook, training)
algorithm lloyd isinput: codebook to improve, set of training vectors trainingoutput: improved codebook doprevious-codebook ← codebookclusters ← divide training into |codebook| clusters, where each cluster contains all vectors in training who are best represented by the corresponding vector in codebookfor each cluster cluster in clustersdo the corresponding code vector in codebook ← the centroid of all training vectors in clusterwhile difference in error representing training between codebook and previous-codebook > 𝜖 returncodebook
Referencesedit
^Linde, Y.; Buzo, A.; Gray, R. (1980). "An Algorithm for Vector Quantizer Design". IEEE Transactions on Communications. 28 (1): 84–95. doi:10.1109/TCOM.1980.1094577. ISSN 0090-6778. S2CID 18530691. Retrieved 2023-12-28.
^Gray, R.; Gersho, A. (1992). Vector Quantization and Signal Compression (1 ed.). Springer. doi:10.1007/978-1-4615-3626-0. ISBN978-1-4613-6612-6.
January 01, 1970
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