Quality assessment of retargeted images using deep learning capabilities

To display images on panels and screens of different dimensions, there is a need to create algorithms that help adjust them to desired sizes through image retargeting (IR). In this sense, choosing the right algorithms seems to be a challenge. From this perspective, this paper was to propose a method for the assessment of retargeted images using deep learning (DL) models. To this end, the Conditional Random Fields as Recurrent Neural Networks (CRF-RNN) approach was applied to find image pixels in terms of importance and, the You-Only-Look-Once (YOLO) model was employed to identify semantic objects of images. As well, image patch similarity was computed based on Siamese Neural Network (SNN), and output image distortion was recognized by the CNN model. With reference to three well-known databases, viz., MIT RetargetMe, NRID and CUHK, the assessment results of the proposed algorithm demonstrated its superior usage as compared to the existing ones.