Lossy/lossless region-of-interest image coding based on set partitioning in hierarchical trees

We have incorporated a region-of-interest (RoI) coding functionality into Said and Pearlman´s (see IEEE. Trans. CSVT, vol.6, p.243-50, 1996) SPIHT coding with integer transforms. By placing a higher emphasis on the transform coefficients pertaining to the RoI, the RoI is coded with higher fidelity than the rest of the image in earlier stages of progressive reconstruction thus the “important” part of the image is reconstructed more quickly than the rest of the image. This method significantly saves transmission time and storage space by terminating encoding or transmission in situations where the RoI needs to be coded losslessly and the rest of the image visually losslessly (lossy). In our model, the RoI can be flexibly specified either in the beginning or in the middle of the encoding process (either on the original image or on the full- or low-resolution image reconstructed by the decoder), through interaction with the user at the transmitting or the receiving end. Also, the speed with which the quality of the RoI improves in progressive decoding is flexibly specified by the user at either end. The proposed method is especially advantageous in an application where the image is browsed interactively, e.g. telemedicine