scholarly journals A Robust No-Reference, No-Parameter, Transform Domain Image Quality Metric for Evaluating the Quality of Color Images

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 10979-10985 ◽  
Author(s):  
Karen Panetta ◽  
Arash Samani ◽  
Sos Agaian
Keyword(s):  
Image Quality ◽  
Color Images ◽  
Transform Domain ◽  
Quality Metric ◽  
Image Quality Metric

scholarly journals Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

2021 ◽  
Vol 11 (10) ◽  
pp. 4661
Author(s):  
Aladine Chetouani ◽  
Marius Pedersen
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Feature Vector ◽  
Pearson Correlation ◽  
Image Database ◽  
Viewing Distance ◽  
Quality Metric ◽  
Image Quality Metric ◽  
Fully Connected

An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability.

scholarly journals Iterative Phase-Only Hologram Generation Based on the Perceived Image Quality

2019 ◽  
Vol 9 (20) ◽  
pp. 4457 ◽  
Author(s):  
Haining Yang ◽  
Daping Chu
Keyword(s):  
Image Quality ◽  
Mean Squared Error ◽  
Quality Metrics ◽  
Critical Element ◽  
Image Content ◽  
Quality Metric ◽  
Squared Error ◽  
Image Quality Metrics ◽  
Image Quality Metric

Image quality metrics are a critical element in the iterative Fourier transform algorithms (IFTAs) for the computer generation of phase-only holograms. Conventional image quality metrics such as root-mean-squared error (RMSE) are sensitive to image content and unable to reflect the perceived image quality accurately. This would have a significant impact on the calculation speed and the quality of the generated hologram. In this work, the structure similarity (SSIM) was proposed as an image quality metric in IFTAs due to its ability to predict the perceived image quality in the presence of the white Gaussian noise and its independence on the image content. This would enable IFTAs to terminate when further iterations would no longer lead to improvement in the perceived image quality. As a result, up to 75% of unnecessary iterations were eliminated by the use of SSIM as the image quality metric.

How re-training process affect the performance of no-reference image quality metric for face images

2019 ◽  
Vol 2019 (5) ◽  
pp. 528-1-528-6
Author(s):  
Xinwei Liu ◽  
Christophe Charrier ◽  
Marius Pedersen ◽  
Patrick Bours
Keyword(s):  
Image Quality ◽  
Reference Image ◽  
Training Process ◽  
Quality Metric ◽  
Face Images ◽  
Image Quality Metric
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