Feedback-based quantization of color images

Minimizing visible distortion in a quantized color image is context-dependent. Our feedback- based strategy for color image quantization looks at the quantized image as well as the original. This comparison yields useful information to guide the embedded quantization algorithm to devote, during re-quantization of the original image, more resources to areas where the most offensive distortion occurred. Our current implementation of this new strategy uses an edge detector in a scaled RGB space to reveal the location and severeness of false contours, which appear in the quantized image but not in the original. The result of this false- contour detection step is used to identify uniformly colored regions in the quantized image that are along side of significant false contours. These regions correspond directly to areas in the original image that need to be better preserved during re-quantization. A well-known divisive method and our own agglomerative method are adapted separately as the embedded quantization algorithm to demonstrate the applicability and effectiveness of this feedback-based approach.