Image compression using self-organization networks
O.T.-C. Chen, B.J. Sheu, W.-C. Fang · IEEE Transactions on Circuits and Systems for Video Technology · 1994
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.>