Title :
Vector quantization for image compression using circular structured self-organization feature map
Author :
Yamamoto, Takashi
Author_Institution :
Dept. of Electr. & Comput. Eng., Maryland Univ., College Park, MD, USA
Abstract :
We propose a stable and robust vector quantization coding scheme for image compression known as circular self organization feature map (CSOM) by introducing circular structure to a basic codebook. This structure enables the self organization feature map (SOM) method to converge faster, and to learn input vectors more efficiently. The results suggest that CSOM gains approximately 30% speedup in computation time and 0.3 dB in the PSNR compared to the conventional SOM algorithm. In addition, robustness for initial state of a codebook is achieved by CSOM
Keywords :
data compression; image coding; learning (artificial intelligence); self-organising feature maps; table lookup; vector quantisation; CSOM; SOM; circular codebook structure; circular self organization feature map; coding scheme; convergence; image compression; input vector learning; self organization feature map; speedup; vector quantization; Books; Computational efficiency; Educational institutions; Euclidean distance; Frequency; Image coding; Image converters; PSNR; Robustness; Vector quantization;
Conference_Titel :
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location :
Thessaloniki
Print_ISBN :
0-7803-6725-1
DOI :
10.1109/ICIP.2001.958523
