Linde–Buzo–Gray algorithm
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 | 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. (December 2023) |
 | This article relies largely or entirely on a single source. (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 iterative vector 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
Description
The Linde–Buzo–Gray algorithm may be implemented as follows:
algorithm linde-buzo-gray is
input: set of training vectors training, codebook to improve old-codebook
output: codebook that is twice the size and better or as good as old-codebook
new-codebook ← {}
for each old-codevector in old-codebook do
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 is
input: codebook to improve, set of training vectors training
output: improved codebook
do
previous-codebook ← codebook
clusters ← divide training into |codebook| clusters, where each cluster contains all vectors in training who are best represented by the corresponding vector in codebook
for each cluster cluster in clusters do
the corresponding code vector in codebook ← the centroid of all training vectors in cluster
while difference in error representing training between codebook and previous-codebook > 𝜖
return codebook
References
- ^ 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.
- ^ Gray, R.; Gersho, A. (1992). Vector Quantization and Signal Compression (1 ed.). Springer. doi:10.1007/978-1-4615-3626-0. ISBN 978-1-4613-6612-6.