Perceptually Based Downscaling of Images
A. C. Oztireli, M. GrossProceedings of ACM SIGGRAPH (Los Angeles, California, USA, August 9-13, 2015), ACM Transactions on Graphics, vol. 34, no. 4, pp. 77:1-77:10
Abstract
We propose a perceptually based method for downscaling images that provides a better apparent depiction of the input image. We formulate image downscaling as an optimization problem where the difference between the input and output images is measured using a widely adopted perceptual image quality metric. The downscaled images retain perceptually important features and details, resulting in an accurate and spatio-temporally consistent representation of the high resolution input. We derive the solution of the optimization problem in closed-form, which leads to a simple, efficient and parallelizable implementation with sums and convolutions. The algorithm has running times similar to linear filtering and is orders of magnitude faster than the state-of-the-art for image downscaling. We validate the effectiveness of the technique with extensive tests on many images, video, and by performing a user study, which indicates a clear preference for the results of the new algorithm.
Overview
We propose a perceptually based method for downscaling images that provides a better apparent depiction of the input image. We formulate image downscaling as an optimization problem where the difference between the input and output images is measured using a widely adopted perceptual image quality metric. The downscaled images retain perceptually important features and details, resulting in an accurate and spatio-temporally consistent representation of the high resolution input. We derive the solution of the optimization problem in closed-form, which leads to a simple, efficient and parallelizable implementation with sums and convolutions. The algorithm has running times similar to linear filtering and is orders of magnitude faster than the state-of-the-art for image downscaling. We validate the effectiveness of the technique with extensive tests on many images, video, and by performing a user study, which indicates a clear preference for the results of the new algorithm.
