Aiming at the universal No-Reference Image Quality Assessment （NR-IQA） algorithms， a new NR-IQA algorithm based on the saliency deep features of the pseudo reference image was proposed. Firstly， based on the distorted image， the corresponding pseudo reference image of the distorted image generated by ConSinGAN model was used as compensation information of the distorted image， thereby making up for the weakness of NR-IQA methods： lacking real reference information. Secondly， the saliency information of the pseudo reference image was extracted， and the pseudo saliency map and the distorted image were input into VGG16 netwok to extract deep features. Finally， the obtained deep features were merged and mapped into the regression network composed of fully connected layers to obtain a quality prediction consistent with human vision.Experiments were conducted on four large public image datasets TID2013， TID2008， CSIQ and LIVE to prove the effectiveness of the proposed algorithm. The results show that the Spearman Rank-Order Correlation Coefficient （SROCC） of the proposed algorithm on the TID2013 dataset is 5 percentage points higher than that of H-IQA （Hallucinated-IQA） algorithm and 14 percentage points higher than that of RankIQA （learning from Rankings for no-reference IQA） algorithm. The proposed algorithm also has stable performance for the single distortion types. Experimental results indicate that the proposed algorithm is superior to the existing mainstream Full-Reference Image Quality Assessment （FR-IQA） and NR-IQA algorithms， and is consistent with human subjective perception performance.

In the production of printing industry， using You Only Look Once version 4 （YOLOv4） directly to detect printing defect targets has low accuracy and requires a large number of training samples. In order to solve the problems， a defect target detection method for printed matter based on Siamese-YOLOv4 was proposed. Firstly， a strategy of image segmentation and random parameter change was used to enhance the dataset. Then， the Siamese similarity detection network was added to the backbone network， and the Mish activation function was introduced into the similarity detection network to calculate the similarity of image blocks. After that， the regions with similarity below the threshold were regarded as the defect candidate regions. Finally， the candidate region images were trained to achieve the precise positioning and classification of defect targets. Experimental results show that， the detection precision of the proposed Siamese-YOLOv4 model is better than those of the mainstream target detection models. On the printing defect dataset， the Siamese-YOLOv4 network has the detection precision for satellite ink droplet defect of 98.6%， the detection precision for dirty spot of 97.8%， the detection precision for print lack of 93.9%； and the mean Average Precision （mAP） reaches 96.8%， which is 6.5 percentage points，6.4 percentage points， 14.9 percentage points and 10.6 percentage points higher respectively than the YOLOv4 algorithm， the Faster Regional Convolutional Neural Network （Faster R-CNN） algorithm， the Single Shot multibox Detector （SSD） algorithm and the EfficientDet algorithm. The proposed Siamese-YOLOv4 model has low false positive rate and miss rate in the defect detection of printed matter， and improves the detection precision by calculating similarity of the image blocks through the similarity detection network， proving that the proposed defect detection method can be applied to the printing quality inspection and therefore improve the defect detection level of printing enterprises.