Learning to Assess Image Retargeting

Abstract

Content-aware image retargeting enables images to fit different devices with various aspect ratios while preserving salient contents. Meanwhile, assessing the quality of image retargeting and unifying both subjective and objective evaluation have become a prominent challenge. In this paper, we propose an image quality assessment based on Radial Basis Function (RBF) neural network. We propose a new feature of image retargeting evaluation, which adapts structural similarity (SSIM) and saliency. By also including other existing features, we build a neural network to assess the quality of the retargeted image. The neural network is trained to combine the above-mentioned features. The accuracy of our proposed assessment is verified by simulations and it possesses huge practical significance.