In vector quantization, the codebook generation problem can be
formulated as a classification problem of dividing N
$_{p}$
training vectors into N
$_{c}$
clusters, where
N
$_{p}$
is the training size of input vectors and
N
$_{c}$
is the codeword size of codebook. For large
N
$_{p}$
and N
$_{c}$
, a traditional search
algorithmsuch as the LBG method can hardly find the global optimal
classification and needs a great deal of calculation. In this paper, a novel VQ
codebook generation method based on Otsu histogram threshold is proposed. The
computational complexity of squared Euclidean distance can be reduced to
O(N
$_{p}$
log
$_{2}$
N
$_{c}$
)
for a codebook with gray levels. Our method provides better image quality than
recent proposed schemes in high compression ratio. The experimental results and
the comparisons show that this method can not only reduce the computational
complexity of squared Euclidean distance but also find better codewords to
improve the quality of the resulted VQ codebook.
