Naslov
Local Thresholding Classified Vector Quantization With Memory Reduction

Autori
Dujmić, Hrvoje ; Rožić, Nikola ; Begušić, Dinko ; Ursić, Jurica

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the International Workshop on Image and Signal Processing and Analysis / Lončarić, Sven - Zagreb : Sveučilišni računski centar Sveučilišta u Zagrebu (Srce), 2000, 197-202

Skup
International Workshop on Image and Signal Processing and Analysis (in conjuction with 22nd Int. Conference on Information Technology Interfaces - ITI2000)

Mjesto i datum
Pula, Hrvatska, 13.06.2000. - 16.06.2000

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
classified vector quantization; memory reduction

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA