Near-reversible efficient image resizing for devices supporting different spatial resolutions

Many devices in cloud environments support different spatial resolutions, necessitating image resizing of the original image contents. The goal of this paper is to combine multiple operators for image resizing in a stochastic optimization framework, seeking an optimal balance among essential resizing operators such as cropping and linear scaling. Specifically, we formulate image resizing as a MAP (maximum a posteriori) optimization problem with a Gibbs energy function. To reduce computational complexity we seek a sub-optimal solution of the MAP criterion with a deterministic implementation of the Metropolis algorithm. Since the optimization is carried out on the basis of a straight horizontal or vertical line in an image instead of the curved seam pixels, the optimization should converge quickly to have a fast image resizing. In addition, our image resizing can be associated with various user-defined content filtering such as a color masking. Finally, our resizing method is reversible, meaning that the image with the original size can be reconstructed from the retargeted image. This allows us to apply the proposed image resizing method to a prioritized image transmission with a scalable and progressive structure.