Image Compression Based on Importance Using Optimal Mass Transportation Map

Demand for efficient image transmission and storage is in-creasing rapidly because of the continuing growth of multi-media technology and VR and AR applications. In this paper, we proposed an image compression method based on the recognition of importance of regions in images. As not all the information in an image is equally useful, we can identify important regions in an image for high fidelity compression and accept a comparatively more lossy compression about less important regions of the image. First, we segment images to two parts, namely, foreground and background, where the foreground represents the more important component and the background is of less importance. Second, we apply optimal mass transportation mapping in a GAN (generative adversarial network) framework to both the foreground and back-ground to magnify the foreground and shrink the background while keeping the shape and total image area unchanged. As a result, in the processed image, the ratio of foreground to background is larger than the corrresponding ratio in the original image. This ratio is controllable in our process, giving users the ability to control the degree of compression. The GAN-processed image is then used for compression. To restore the image, we apply a GAN model to the compressed image and recover the ratio of foreground and background using an optimal mass transportation map. Test results show that our method is highly effective in reconstructing detail of important components in compressed images while achieving a high compression ratio.