Title :
Image compression using self-organization networks
Author :
Chen, Oscal T C ; Sheu, Bing J. ; Fang, Wai-Chi
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
fDate :
10/1/1994 12:00:00 AM
Abstract :
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
Keywords :
data compression; image coding; learning (artificial intelligence); self-organising feature maps; vector quantisation; adaptivity; centroid learning rule; codebooks; frequency-sensitive cost function; image compression; performance; self-organization networks; self-organization neural network architecture; source data; vector quantization; Artificial neural networks; Biological neural networks; Cost function; Frequency; Image coding; Image processing; Information processing; Neural networks; Statistics; Vector quantization;
Journal_Title :
Circuits and Systems for Video Technology, IEEE Transactions on
