scholarly journals No reference Image Quality Measure for Hazy Images

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.

A SPATIAL DOMAIN OBJECT SEPARABILITY BASED NO-REFERENCE IMAGE QUALITY MEASURE USING MEAN AND VARIANCE

2013 ◽  
Vol 13 (02) ◽  
pp. 1340005 ◽  
Author(s):  
KANJAR DE ◽  
V. MASILAMANI
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Quality Measure ◽  
Reference Image ◽  
Individual Object ◽  
The Difference ◽  
Image Quality Measure ◽  
Good Quality Image ◽  
Degraded Images

In many modern image processing applications determining quality of the image is one of the most challenging tasks. Researchers working in the field of image quality assessment design algorithms for measuring and quantifying image quality. The human eye can identify the difference between a good quality image and a noisy image by simply looking at the image, but designing a computer algorithm to automatically determine the quality of an image is a very challenging task. In this paper, we propose an image quality measure using the concept of object separability. We define object separability using variance. Two objects are very well separated if variance of individual object is less and mean pixel values of neighboring objects are very different. Degradation in images can be due to a number of reasons like additive noises, quantization defects, sampling defects, etc. The proposed no-reference image quality measure will determine quality of degraded images and differentiate between good and degraded images.

scholarly journals NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION

2018 ◽  
Vol 37 (2) ◽  
pp. 105 ◽  
Author(s):  
Kanjar De ◽  
Masilamani V
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Multiple Sources ◽  
Noise Image ◽  
Assessment Techniques ◽  
Image Quality Measure ◽  
Channel Errors

Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available. 

Blind Assessment of Wavelet-Compressed Images Based On Subband Statistics of Natural Scenes

2014 ◽  
Vol 6 (1) ◽  
pp. 26-43
Author(s):  
Yingchun Guo ◽  
Gang Yan ◽  
Cuihong Xue ◽  
Yang Yu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Subjective Assessment ◽  
Reference Image ◽  
Natural Scenes ◽  
Compressed Images

This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.

scholarly journals Underwater No-Reference Image Quality Assessment for Display Module of ROV

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Di Wu ◽  
Fei Yuan ◽  
En Cheng
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Water Medium ◽  
Display Module ◽  
Optical Images ◽  
Underwater Image ◽  
Imaging Characteristics ◽  
Monitor Image

The optical images collected by remotely operated vehicles (ROV) contain a lot of information about underwater (such as distributions of underwater creatures and minerals), which plays an important role in ocean exploration. However, due to the absorption and scattering characteristics of the water medium, some of the images suffer from serious color distortion. These distorted color images usually need to be enhanced so that we can analyze them further. However, at present, no image enhancement algorithm performs well in any scene. Therefore, in order to monitor image quality in the display module of ROV, a no-reference image quality predictor (NIPQ) is proposed in this paper. A unique property that differentiates the proposed NIPQ metric from existing works is the consideration of the viewing behavior of the human visual system and imaging characteristics of the underwater image in different water types. The experimental results based on the underwater optical image quality database (UOQ) show that the proposed metric can provide an accurate prediction for the quality of the enhanced image.

scholarly journals DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6457
Author(s):  
Hayat Ullah ◽  
Muhammad Irfan ◽  
Kyungjin Han ◽  
Jong Weon Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Reference Image ◽  
Primary Concern ◽  
Panoramic Images ◽  
Stitching Errors

Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

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 NO-REFERENCE IMAGE QUALITY ASSESSMENT FOR ZY3 IMAGERY IN URBAN AREAS USING STATISTICAL MODEL

2016 ◽  
Vol XLI-B3 ◽  
pp. 949-954 ◽  
Author(s):  
Y. Zhang ◽  
W.H. Cui ◽  
F. Yang ◽  
Z.C. Wu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Urban Areas ◽  
Satellite Images ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Atmospheric Conditions ◽  
Natural Scene Statistics ◽  
The Impact

More and more high-spatial resolution satellite images are produced with the improvement of satellite technology. However, the quality of images is not always satisfactory for application. Due to the impact of complicated atmospheric conditions and complex radiation transmission process in imaging process the images often suffer deterioration. In order to assess the quality of remote sensing images over urban areas, we proposed a general purpose image quality assessment methods based on feature extraction and machine learning. We use two types of features in multi scales. One is from the shape of histogram the other is from the natural scene statistics based on Generalized Gaussian distribution (GGD). A 20-D feature vector for each scale is extracted and is assumed to capture the RS image quality degradation characteristics. We use SVM to learn to predict image quality scores from these features. In order to do the evaluation, we construct a median scale dataset for training and testing with subjects taking part in to give the human opinions of degraded images. We use ZY3 satellite images over Wuhan area (a city in China) to conduct experiments. Experimental results show the correlation of the predicted scores and the subjective perceptions.

A No-Reference Image Quality Comprehensive Assessment Method

2020 ◽  
pp. 2154011
Author(s):  
Yuan-Yuan Fan ◽  
Ying-Jun Sang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Rank Order ◽  
Comprehensive Assessment ◽  
Assessment Model ◽  
Reference Image ◽  
Human Visual Perception ◽  
Function Model

On the basis of the research status of image quality comprehensive assessment, a no-reference image quality comprehensive assessment function model is proposed in this paper. First, the image quality is classified as contrast, sharpness, and signal-to-noise ratio (SNR), and the interrelation of each assessment index is researched and analyzed; second, the weights in the comprehensive assessment model are studied when only contrast, sharpness, and SNR are changed. Finally, on the basis of studying each kind of distortion separately, and considering the different types of image distortion, we studied how to determine the weights of each index in the comprehensive image quality assessment. The results show that the no-reference image quality comprehensive assessment function model proposed in this paper can better fit human visual perception, and it has a good correlation with Difference Mean Opinion Score (DMOS). Correlation Coefficient (CC) reached 0.8331, Spearman Rank Order Correlation Coefficient (SROCC) reached 0.8206, Mean Absolute Error (MAE) was only 0.0920, Root Mean Square Error (RMSE) was only 0.1122, Outlier Ratio (OR) was only 0.0365. The method proposed in this paper can be applied to photoelectric measurement equipment television system and give an accurate and reliable quality assessment to no reference television images.

Blind Assessment of Wavelet-Compressed Images Based on Subband Statistics of Natural Scenes

2018 ◽  
pp. 1322-1337
Author(s):  
Yingchun Guo ◽  
Gang Yan ◽  
Cuihong Xue ◽  
Yang Yu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Subjective Assessment ◽  
Natural Scenes ◽  
Assessment Scores ◽  
Compressed Images

This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.

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

Algorithms ◽  
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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