TY - JOUR
T1 - Multiresolution, dynamic, and adaptive image quantization methodology
T2 - Automation and analysis
AU - Soh, Leen Kiat
N1 - Funding Information:
The research was sponsored in part by Naval Research Laboratory contract N00014-95-C-6038. The author would like to thank Costas Tsatsoulis for improving the paper and the anonymous reviewers for their insightful and useful comments.
PY - 2003/4
Y1 - 2003/4
N2 - We describe a multiresolution, dynamic, and adaptive image quantization methodology with automation being the goal of our research. To improve the robustness of the approach, we incorporate dynamic local thresholding and multiresolution peak detection. The first strategy extracts bisector values from local regions of the image and builds a histogram based on those values. The second strategy maps the derived histogram into multiple levels of resolution, allowing peaks be scored for their significance and localized. We conduct several experiments to analyze different versions of our quantization methodology and to compare it with the equal probability quantization. We also investigated the relationships between image attributes and the key parameters in our quantizers. Based on the findings, we developed a fully automated quantizer called QTR0.5. We have applied QTR0.5 to a variety of images-aerial, photographic, and satellite images-and have also used it as a pre-processor in an image segmentation software tool.
AB - We describe a multiresolution, dynamic, and adaptive image quantization methodology with automation being the goal of our research. To improve the robustness of the approach, we incorporate dynamic local thresholding and multiresolution peak detection. The first strategy extracts bisector values from local regions of the image and builds a histogram based on those values. The second strategy maps the derived histogram into multiple levels of resolution, allowing peaks be scored for their significance and localized. We conduct several experiments to analyze different versions of our quantization methodology and to compare it with the equal probability quantization. We also investigated the relationships between image attributes and the key parameters in our quantizers. Based on the findings, we developed a fully automated quantizer called QTR0.5. We have applied QTR0.5 to a variety of images-aerial, photographic, and satellite images-and have also used it as a pre-processor in an image segmentation software tool.
UR - http://www.scopus.com/inward/record.url?scp=0037721344&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0037721344&partnerID=8YFLogxK
U2 - 10.1117/1.1557158
DO - 10.1117/1.1557158
M3 - Article
AN - SCOPUS:0037721344
SN - 1017-9909
VL - 12
SP - 229
EP - 243
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
IS - 2
ER -