Region-of-Interest Coding Based on Saliency Detection and Directional Wavelet for Remote Sensing Images

With growing contradiction between the high-speed acquisition of remote sensing data and the low-speed data storage and transmission, the advantages of giving higher priority to a region of interest (ROI) in compression have become prominent. Previous research focused on ROI coding, rather than automatic ROI extraction. However, accurate ROI extraction can significantly improve coding efficiency. In this letter, we propose an automatic ROI extraction based on the improved normal directional lifting wavelet transform (LWT). Then, the compression efficiency is enhanced by a novel tangent directional LWT to reduce the signal energy of high-frequency subbands. Finally, the autogenerated ROIs are encoded by a new multibitplane alternating shift method, which supports not only arbitrarily shaped ROI coding, but also flexible adjustment of compression quality in the ROI and the background. The experimental results demonstrate that our method can effectively highlight the ROIs with well-defined boundaries, meanwhile improving the ROI coding with better visual quality.