Online personalized news product needs a suitable cover for the article. The news cover demands to be with high image quality, and draw readers' attention at same time, which is extraordinary challenging due to the subjectivity of the task. In this paper, we assess the news cover from image clarity and object salience perspective. We propose an end-to-end multi-task learning network for image clarity assessment and semantic segmentation simultaneously, the results of which can be guided for news cover assessment. The proposed network is based on a modified DeepLabv3+ model. The network backbone is used for multiple scale spatial features exaction, followed by two branches for image clarity assessment and semantic segmentation, respectively. The experiment results show that the proposed model is able to capture important content in images and performs better than single-task learning baselines on our proposed game content based CIA dataset.
Since the human visual system (HVS) is highly sensitive to edges, a novel image quality assessment (IQA) metric for assessing screen content images (SCIs) is proposed in this paper. The turnkey novelty lies in the use of an existing parametric edge model to extract two types of salient attributes — namely, edge contrast and edge width, for the distorted SCI under assessment and its original SCI, respectively. The extracted information is subject to conduct similarity measurements on each attribute, independently. The obtained similarity scores are then combined using our proposed edge-width pooling strategy to generate the final IQA score. Hopefully, this score is consistent with the judgment made by the HVS. Experimental results have shown that the proposed IQA metric produces higher consistency with that of the HVS on the evaluation of the image quality of the distorted SCI than that of other state-of-the-art IQA metrics.  Objective image video quality metrics which accurately represent the subjective quality of processed images are of paramount importance for the design and assessment of an image compression and transmission system. In some scenarios, it is also important to evaluate the quality of the received image with minimal reference to the transmitted one. For instance, for closed-loop optimization of a transmission system, the image quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image - prior to compression and transmission - is not usually available at the receiver side, and it is important to rely at the receiver side on an objective quality metric that does not need reference or needs minimal reference to the original image. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art reduced reference metric.