Image compression using self-organization networks
IEEE Transactions on circuits and systems for video technology, 1994•ieeexplore.ieee.org
A self-organization neural network architecture is used to implement vector quantization for
image compression. A modified self-organization algorithm, which is based on the frequency-
sensitive cost function and centroid learning rule, is utilized to construct the codebooks.
Performances of this frequency-sensitive self-organization network and a conventional
algorithm for vector quantization are compared. The proposed method is quite efficient and
can achieve near-optimal results. Good adaptivity for different statistics of source data can …
image compression. A modified self-organization algorithm, which is based on the frequency-
sensitive cost function and centroid learning rule, is utilized to construct the codebooks.
Performances of this frequency-sensitive self-organization network and a conventional
algorithm for vector quantization are compared. The proposed method is quite efficient and
can achieve near-optimal results. Good adaptivity for different statistics of source data can …
A self-organization neural network architecture is used to implement vector quantization for image compression. A modified self-organization algorithm, which is based on the frequency-sensitive cost function and centroid learning rule, is utilized to construct the codebooks. Performances of this frequency-sensitive self-organization network and a conventional algorithm for vector quantization are compared. The proposed method is quite efficient and can achieve near-optimal results. Good adaptivity for different statistics of source data can also be achieved.< >
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果