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.

中文翻译：

---

使用深度学习功能评估重定向图像的质量

为了在不同尺寸的面板和屏幕上显示图像，需要创建算法来帮助通过图像重定向（IR）将它们调整到所需的尺寸。从这个意义上说，选择正确的算法似乎是一个挑战。从这个角度来看，本文提出了一种使用深度学习（DL）模型评估重定向图像的方法。为此，应用条件随机场作为循环神经网络（CRF-RNN）方法来查找图像像素的重要性，并采用You-Only-Look-Once（YOLO）模型来识别图像的语义对象。此外，基于连体神经网络（SNN）计算图像块相似度，并通过 CNN 模型识别输出图像失真。参考MIT RetargetMe、NRID和CUHK这三个知名数据库的评估结果，该算法比现有算法具有优越的使用性。