scholarly journals Influence of the distortion type on the image quality assessment when reducing its sizes

2020 ◽  
pp. 22-27
Author(s):  
D. G. Asatryan ◽  
M. E. Harutyunyan ◽  
Y. I. Golub ◽  
V. V. Starovoitov
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Image Database ◽  
The Other ◽  
Gradient Field ◽  
Original Image ◽  
Quality Ratings ◽  
Distortion Type ◽  
Spearman’S Correlation ◽  
Very High

In this paper, the influence of various types of distortion of an image on its quality while reducing its sizes, is investigated. To assess the image quality, it is proposed to use the method of comparison with the standard using a previously developed measure based on the proximity of the values of the parameters of the Weibull distribution, which describes the gradient field of the image. The well-known TID2013 image database was used as the material, which includes 3000 images distorted by 24 types of distorting algorithms with five levels. Each image of the base was reduced by 2, 4 and 8 times by the two most common methods and compared with the original image-original. The calculations were performed for five types of distortions implemented in the database. To make a decision on the acceptability of the applied quality measure, the calculated measure values were compared with the subjective quality ratings provided along with the documentation on the TID2013 database. The comparison was carried out using Spearman’s correlation coefficient. It is shown that the average values of correlations for all images at three types of distortions are very high, while for the other two they are unacceptably low. An attempt has been made to explain this situation by the properties of distorting algorithms that change the structural properties of the image to varying degrees.The possibility of comparing images of the same scene, but with different resolutions, is demonstrated.

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

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.

Image Quality Assessment Based on Invariant Moments Similarity

2012 ◽  
Vol 546-547 ◽  
pp. 565-569
Author(s):  
Mei Wang ◽  
E Ye Wang ◽  
Guo Hua Pan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Experimental Result ◽  
Original Image ◽  
Image Size ◽  
Invariant Moment ◽  
Invariant Moments ◽  
Computing Complexity ◽  
Better Than

To resolve the problems of the image quality assessment issue and the algorithm adaptability for different image size and deformation, this paper proposes a image quality assessment algorithm based on Invariant Moments Similarity. Firstly, Hu invariant moments values of original image and evaluated image are computed. Secondly the invariant moments distance is completed between original image and evaluated image. At last, the method assess the restoration image quality depend on the invariant moment distance. The experimental result shows that the algorithm result is better than MSE, PSNR, SSIM for the same-size images. And the algorithm based on invariant moment similarity can evaluate different image-size and deformation images with low computing-complexity. The assessment experimental result for difference actual images certifies the algorithm effectiveness.

scholarly journals Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Saifeldeen Abdalmajeed ◽  
Jiao Shuhong
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Natural Image ◽  
Natural Scenes ◽  
Signal To Noise ◽  
Enhanced Sensitivity ◽  
Full Reference ◽  
Distortion Type

Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model.

Performance Evaluation of Color Retinal Image Quality Assessment in Asymmetric Channel VQ Coding

2013 ◽  
Vol 4 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Agung W. Setiawan ◽  
Andriyan B. Suksmono ◽  
Tati R. Mengko ◽  
Oerip S. Santoso
Keyword(s):  
Image Quality ◽  
Blood Vessel ◽  
Quality Assessment ◽  
Image Coding ◽  
Channel Coding ◽  
Retinal Image ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
The Other ◽  
Retinal Blood Vessel

The RGB color retinal image has an interesting characteristic, i.e. the G channel contains more important information than the other ones. One of the most important features in a retinal image is the retinal blood vessel structure. Many diseases can be diagnosed based on in the retinal blood vessel, such as micro aneurysms that can lead to blindness. In the G channel, the contrast between retinal blood vessel and its background is significantly high. The authors explore this retinal image characteristic to construct a more suitable image coding system. The coding processes are conduct in three schemes: weighted R channel, weighted G channel, and weighted B channel coding. Their hypothesis is that allocating more bits in the G channel will improve the coding performance. The authors seek for image quality assessment (IQA) metrics that can be used to measure the distortion in retinal image coding. Three different metrics, namely Peak Signal to Noise Ratio (PSNR), Structure Similarity (SSIM), and Visual Information Fidelity (VIF) are compared as objective assessment in image coding and to show quantitatively that G channel has more important role compared to the other ones. The authors use Vector Quantization (VQ) as image coding method due to its simplicity and low-complexity than the other methods. Experiments with actual retinal image shows that the minimum value of SSIM and VIF required in this coding scheme is 0.9940 and 0.8637.

scholarly journals An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

2019 ◽  
Vol 8 (9S2) ◽  
pp. 588-601 ◽  
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Image Quality ◽  
Image Quality Assessment ◽  
Color Image ◽  
Color Images ◽  
Original Image ◽  
High Quality ◽  
Noise Models ◽  
Single Approach

Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image, should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that works for color images with single and mixed noises. It also suggests the best filter for color that involve in producing a high-quality color image. The metrics like PSNR, Entropy, SSIM, MSE, FSIM, and EPI are considered as image quality assessment metric

scholarly journals Up-Scaling of the Compressed Images using Adaptive Image Interpolation

2019 ◽  
Vol 8 (11) ◽  
pp. 2263-2266
Keyword(s):  
Image Quality ◽  
Electronic Devices ◽  
Image Interpolation ◽  
The Other ◽  
Original Image ◽  
Standard Methods ◽  
Compressed Images ◽  
Image Pixels ◽  
Up Scaling

In this paper, a method of image up scaling has been proposed. In many applications the display of electronic devices needs up scaling of images, but some time size of original image is small, so it may require synthetically increasing the size of the image. Interpolation is one of the solutions for that. In the presented method, a new up-scaled image has been achieved, which is upgraded based on the original image pixels present in small images. In the paper, its working is defined for the different directions of the image. The well-known image quality matrices have been considered to evaluate the performance of proposed method. The method has outperformed than the other standard methods and results have been shown in the paper

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