Title :
Use of nonlinear principal component analysis and vector quantization for image coding
Author :
Tzovaras, D. ; Strintzis, M.G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Thessaloniki Univ.
fDate :
8/1/1998 12:00:00 AM
Abstract :
The nonlinear principal component analysis (NLPCA) method is combined with vector quantization for the coding of images. The NLPCA is realized using the backpropagation neural network (NN), while vector quantization is performed using the learning vector quantizer (LVQ) NN. The effects of quantization in the quality of the reconstructed images are then compensated by using a novel codebook vector optimization procedure
Keywords :
backpropagation; data communication; image coding; image reconstruction; neural nets; optimisation; transform coding; vector quantisation; backpropagation neural network; codebook vector optimization; data compression; image coding; learning vector quantizer; nonlinear principal component analysis; reconstructed image quality; transform coefficients; vector quantization; Data compression; Discrete cosine transforms; Discrete transforms; Image coding; Image storage; Karhunen-Loeve transforms; Neural networks; Principal component analysis; Space technology; Vector quantization;
Journal_Title :
Image Processing, IEEE Transactions on
