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 preprocessor in an image segmentation software tool.