This research describes an image representation technique that
             entails progressive refinement of user specified regions of
             interest (ROI) of large images. Progressive refinement of
             original quality can be accomplished in theory. However, due to
             heavy burden on storage resources for our applications, we
             restrict the refinement to about 25% of the original data
             resolution. Wavelet decomposition with Vector Quantization (VQ)
             of the high frequency components and JPEG/DCT compression of low
             frequency component is used as representation framework. Our
             software will reconstruct the region selected by the user from
             its wavelet decomposition at desired resolution. Further
             refinement from the first preview can be obtained progressively
             by transmitting high frequency coefficients from low resolution
             to high resolution, which are compressed by variant of Vector
             Quantization called Model Based Vector Quantization. The user
             will have an option of progressive build up of the ROI's until
             full resolution stored or terminate
             the transmission at any time during the progressive refinement.
             The entire architecture of the program is based on object
             oriented programming using C++. A multiresolution decomposition
             into wavelet coefficients provides the most useful image
             representation or image browsing. Image decomposition is done
             recursively into wavelet coefficients using the technique [2]
             shown in Figure 1. The given image is decomposed into low and
             high frequency bands along the rows and columns.
             The high frequency subbands are first scalar quantized and the
             low frequency band, LL subband of the highest-level
             decomposition is compressed using JPEG/DCT technique. The scalar
             quantization is heavier for higher resolutions and reduced
             successively for low resolution high frequency components. The
             coefficients are then further compressed by a variant of Vector
             ...