The classic signal quantization problem was introduced by Lloyd. We formulate another, similar problem: The optimal mapping of digital fine grayscale images (such as 9–13 bits-per-pixel medical images) to a coarser scale (e.g., 8 bits per pixel on conventional computer monitors). While the former problem is defined basically in the real signal domain with smoothly distributed noise, the latter refers to an essentially digital domain. As we show in this paper, it is this difference that makes the classic quantization methods virtually inapplicable in typical cases of quantization of the already digitized images. We found experimentally that an algorithm based on dynamic programming provides significantly better results than Lloyd’s method. Data representation and content description are two basic components required by the management of any image database. A wavelet-based system, called the Waveguide, which integrates these two components in a unified framework, is proposed in this work. This system presents a novel technique for unsupervised change analysis that leads to a method of ranking the changes that occur between two satellite images acquired at different moments of time in this project. The existing system of change analysis used Change Vector analysis Technique. This framework based on the representation of the CVA in polar coordinates. The proposed system of change analysis is based on binary descriptors and uses the Hamming distance as a similarity metric.