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

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.

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Contrast-distorted image quality assessment based on curvelet domain features

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i3.pp2595-2603 ◽

2021 ◽

Vol 11 (3) ◽

pp. 2595

Author(s):

Ismail Taha Ahmed ◽

Chen Soong Der ◽

Baraa Tareq Hammad ◽

Norziana Jamil

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Pearson Correlation ◽

Poor Performance ◽

Spatial Domain ◽

Reference Image ◽

Quality Metric ◽

Image Quality Metric ◽

Fold Cross Validation

Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain features are the basis of NR-IQA-CDI architecture. Therefore, in this paper, the spatial domain features are complementary with curvelet domain features, in order to take advantage of the potent properties of the curvelet in extracting information from images such as multiscale and multidirectional. The experimental outcome rely on K-fold cross validation (K ranged 2-10) and statistical test showed that the performance of NR-IQA-CDI rely on curvelet domain features (NR-IQA-CDI-CvT) significantly surpasses those which are rely on five spatial domain features.

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A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps

Algorithms ◽

10.3390/a13120313 ◽

2020 ◽

Vol 13 (12) ◽

pp. 313

Author(s):

Domonkos Varga

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Feature Vector ◽

State Of The Art ◽

Prediction Performance ◽

Perceptual Quality ◽

Reference Image ◽

Full Reference

The goal of full-reference image quality assessment (FR-IQA) is to predict the perceptual quality of an image as perceived by human observers using its pristine (distortion free) reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulting feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is explained. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with a small amount of data to reach high prediction performance. Our best proposal—called ActMapFeat—is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.

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

Applied Sciences ◽

10.3390/app9204457 ◽

2019 ◽

Vol 9 (20) ◽

pp. 4457 ◽

Cited By ~ 1

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.

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

IEEE Access ◽

10.1109/access.2018.2804901 ◽

2018 ◽

Vol 6 ◽

pp. 10979-10985 ◽

Cited By ~ 9

Author(s):

Karen Panetta ◽

Arash Samani ◽

Sos Agaian

Keyword(s):

Image Quality ◽

Color Images ◽

Transform Domain ◽

Quality Metric ◽

Image Quality Metric

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No reference Image Quality Measure for Hazy Images

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2020.1231.41 ◽

2020 ◽

Vol 13 (6) ◽

pp. 460-471

Author(s):

Ahmed Hashim ◽

◽

Hazim Daway ◽

Hana kareem ◽

◽

...

Keyword(s):

Image Quality ◽

Correlation Coefficient ◽

Image Quality Assessment ◽

Pearson Correlation ◽

Human Perception ◽

Reference Image ◽

Special Device ◽

Image Quality Measure ◽

Subjective Study

Haze causes the degradation of image quality. Thus, the quality of the haze must be estimated. In this paper, we introduce a new method for measuring the quality of haze images using a no-reference scale depending on color saturation. We calculate the probability for a saturation component. This work also includes a subjective study for measuring image quality using human perception. The proposed method is compared with other methods as, entropy, Naturalness Image Quality Evaluator (NIQE), Haze Distribution Map based Haze Assessment (HDMHA), and no reference image quality assessment by using Transmission Component Estimation (TCE). This done by calculating the correlation coefficient between non-reference measures and subjective measure, the results show that the proposed method has a high correlation coefficient values for Pearson correlation coefficient (0.8923), Kendall (0.7170), and Spearman correlation coefficient (0.8960). The image database used in this work consists of 70 hazy images captured by using a special device, design to capture haze image. The experiment on haze database is consistent with the subjective experiment.

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