Title :
Image compression via multiresolution feature-based VQ of Hermite-binomial transform coefficients using Kohonen neural network
Author :
De Argandona, I. Ruiz ; Gu, Y.-H. ; Carrasco, R.A.
Author_Institution :
Staffordshire Polytech., Stafford, UK
Abstract :
A new effective feature-based resolution-based vector quantisation (VQ) method for Hermite binomial transform domain image coding is proposed. Hermite-binomial transform is known to be highly relevant to the human receptive field due to its association to the Gaussian derivative models. However, there are more transform coefficients than the original image samples due to the requirement of overlapped windows, which hindered its application to image coding. In the proposed scheme, we apply several VQ subcodebooks to encode image edge profiles and textures at different resolution levels. Simulation results on image coding showed that a high compression ratio can be obtained with good visual quality. Results have also been compared with that of JPEG image coding
Keywords :
feature extraction; image coding; image resolution; image sampling; image texture; self-organising feature maps; transform coding; vector quantisation; Gaussian derivative models; Hermite binomial transform domain; Hermite-binomial transform coefficients; JPEG; Kohonen neural network; VQ subcodebooks; high compression ratio; human receptive field; image coding; image compression; image edge profiles; image samples; image textures; multiresolution feature-based VQ; overlapped windows; simulation results; visual quality;
Conference_Titel :
Image Processing and its Applications, 1995., Fifth International Conference on
Conference_Location :
Edinburgh
Print_ISBN :
0-85296-642-3
DOI :
10.1049/cp:19950719
