Local Thresholding Classified Vector Quantization With Memory Reduction (CROSBI ID 480570)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Dujmić, Hrvoje ; Rožić, Nikola ; Begušić, Dinko ; Ursić, Jurica
engleski
Local Thresholding Classified Vector Quantization With Memory Reduction
In this paper a new memory reduction method for classified vector quantization (CVQ) is presented. Symmetry reflection, rotation and inversion of edge subimages are used to join appropriate edge classes thus reducing memory requirements for edge codebooks by 8(4) times for the classifier used in this paper. Besides the memory reduction, our method generates the more robust codebooks thus increasing PSNR for images outside the training set. It also relieves codebook generation for high bit rate by reducing the number of images that should be inside the training set. The proposed method has been tested with classifier that is based on the comparison of locally thresholded image vectors with a predefined set of binary edge templates.
classified vector quantization; memory reduction
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Podaci o prilogu
197-202-x.
2000.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the International Workshop on Image and Signal Processing and Analysis
Lončarić, Sven
Zagreb: Sveučilišni računski centar Sveučilišta u Zagrebu (Srce)
Podaci o skupu
International Workshop on Image and Signal Processing and Analysis (in conjuction with 22nd Int. Conference on Information Technology Interfaces - ITI2000)
predavanje
13.06.2000-16.06.2000
Pula, Hrvatska