Research On Algorithm Of Encoding ROI Image In The Network Transmission

With the development of information technology, more people now have access to information through internet. In fact, network environment has now become congested and unstable. This instability has influenced the quality of images which has lead to poor image restoration. In areas where there are special requirements, and a little bit of image defects will lead to irreparable consequences. For example, a variety of medical images has become a very intuitive diagnosis when the two clients make a remote medical consultation in medical image science. Currently, the research for coding of the region of interest only stays in the extraction, and often leads to complexity of coding increasing and leads low efficiency of transmission.In this research work, we propose a new algorithm called multi-priority multi-ROI coding which uses the definition of coding block, the quality level and packaging bag in EBCOT algorithm that will reorder the packets of ROI and background area in the bit planes and insert some new quality layers. All interested area need to execute wavelet transform to n+1 times and the data packets are encoded into the corresponding layers according to the order of priority, while it encodes the region of background into the final packages. If the code stream send a higher quality Packets of ROI without a background, then the displayed image showed the priority area of interest which is first encoded and transported and thus the picture received is without background. This phenomenon is referred to as priority-based progressive transmission of regions of interest mode.We design simulation experiments based on Digital Image Processing Toolbox of Matlab and compare our scheduling algorithms against non-ROI algorithm and Maxshift algorithm. Experiments results show that recovery time on a single ROI and multiple ROIs of MPMR is lower than others and MPMR can achieve ROIs at the decoder in accordance with the order of priority rapidly. MPMR achieves ROIs with high-resolution. As algorithm of MPMR only changes data packets of the image, so it does not increase the complexity. In addition, it proved that ROI percentage of the total chart area impact on MPMR algorithm greatly by experiments. The effect is obvious when the ROI area is below 40% the size of the picture.