Non-uniform image compression using biologically motivated saliency map model

We propose a new non-uniform image compression using a biologically motivated selective attention model for effective storage and transmission of natural images. One of the important issues in non-uniform image compression is to decide on a meaningful region according to a purpose. The proposed saliency map model can generate a scan path that contains plausible interesting objects in a natural scene. The proposed non-uniform image compression method uses the saliency map results, which differently compress the selected interesting areas and the uninteresting areas by a lossless coding algorithm and lossy compression, respectively. Experimental results show that the proposed non-uniform compression method gives better peak signal to noise ratio (PSNR) but slightly decreases the compression ratio.