Wavelet-Based Image Compression with Polygon-Shaped Region of Interest

A wavelet-based lossy-to-lossless image compression technique with polygon-shaped ROI function is proposed. Firstly, split and mergence algorithms are proposed to separate concave ROIs into smaller convex ROIs. Secondly, row-order scan and an adaptive arithmetic coding are used to encode the pixels in ROIs. Thirdly, a lifting integer wavelet transform is used to decompose the original image in which the pixels in the ROIs have been replaced by zeros. Fourthly, a wavelet-based compression scheme with adaptive prediction method (WCAP) is used to obtain predicted coefficients for difference encoding. Finally, the adaptive arithmetic coding is also adopted to encode the differences between the original and corresponding predicted coefficients. The proposed method only needs less shape information to record the shape of ROI and provides a lossy-to-lossless coding function; thus the approach is suitable for achieving the variety of ROI requirements including polygon-shaped ROI and multiple ROIs. Experimental results show that the proposed lossy-to-lossless coding with ROI function reduces bit rate as comparing with the MAXSHIFT method in JPEG2000; moreover, when the image without ROI is compressed by the proposed lossless coding, the proposed approach can also achieve a high compression ratio.