A deep evaluator for image retargeting quality by geometrical and contextual interaction

An image is compressed or stretched during the multidevice displaying, which will have a very big impact on perception quality. In order to solve this problem, a variety of image retargeting methods have been proposed for the retargeting process. However, how to evaluate the results of different image retargeting is a very critical issue. In various application systems, the subjective evaluation method cannot be applied on a large scale. So we put this problem in the accurate objective-quality evaluation. Currently, most of the image retargeting quality assessment algorithms use simple regression methods as the last step to obtain the evaluation result, which are not corresponding with the perception simulation in the human vision system (HVS). In this paper, a deep quality evaluator for image retargeting based on the segmented stacked AutoEnCoder (SAE) is proposed. Through the help of regularization, the designed deep learning framework can solve the overfitting problem. The main contributions in this framework are to simulate the perception of retargeted images in HVS. Especially, it trains two separated SAE models based on geometrical shape and content matching. Then, the weighting schemes can be used to combine the obtained scores from two models. Experimental results in three well-known databases show that our method can achieve better performance than traditional methods in evaluating different image retargeting results

A Comparative Study of Fixation Density Maps

International audienceFixation density maps (FDM) created from eye tracking experiments are widely used in image processing applications. The FDM are assumed to be reliable ground truths of human visual attention and as such one expects high similarity between FDM created in different laboratories. So far, no studies have analysed the degree of similarity between FDM from independent laboratories and the related impact on the applications. In this paper, we perform a thorough comparison of FDM from three independently conducted eye tracking experiments. We focus on the effect of presentation time and image content and evaluate the impact of the FDM differences on three applications: visual saliency modelling, image quality assessment, and image retargeting. It is shown that the FDM are very similar and that their impact on the applications is low. The individual experiment comparisons, however, are found to be significantly different, showing that inter-laboratory differences strongly depend on the experimental conditions of the laboratories. The FDM are publicly available to the research community