scholarly journals Full Reference-Image Quality Assessment with Global and Local Image Regions for Visual Quality Perception and Optimization

2019 ◽  
Vol 8 (6S4) ◽  
pp. 352-356
Keyword(s):  
Quality Assessment ◽  
Image Quality Assessment ◽  
Optimization Problems ◽  
Visual Quality ◽  
Reference Image ◽  
Mathematical Methods ◽  
Mathematical Properties ◽  
Global And Local ◽  
Local Image

Quality Assessment (IQA) by using mathematical methods is offering favorable results in calculating visual quality of distorted images. These methods are developed by examining effective features that are compatible with characteristics of Human Visual System (HVS). But many of those methods are difficult to apply for optimization problems. This paper presents DCT based metric with easy implementation and having mathematical properties like differentiability, convexity and valid distance metricability to overcome the optimization problems. By using this method we are able to calculate the quality of image as a whole as well as the quality of local image regions.

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 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.

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.

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. 

Analysis and Evaluation of Image Fusion Algorithms

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Neeraj Kumar ◽  
Vikas Kumar Mishra ◽  
C. L.P. Gupta
Keyword(s):  
Image Quality ◽  
Image Fusion ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Imaging Systems ◽  
Reference Image ◽  
Analysis And Evaluation ◽  
Reference Methods ◽  
Objectively Measured

There is an increasing need for performance tools or quality assessment in order to compare the results obtained with different algorithms of image fusion. This analysis can be used to select a specific algorithm for a defined fusion dataset. The image quality is a characteristic of an image that measures the perceived image degradation (typically, compared to an ideal or perfect picture). Imaging systems may introduce a certain amount of distortion or artifacts in the signal, hence the quality assessment is an important problem. There are several techniques and measures that can be objectively measured and evaluated automatically by a computer program. Therefore, they may be classified as complete reference methods (FR) and the No-reference methods (NR). In the methods of image quality assessment FR, the quality of a test image is evaluated by comparing a reference image that is supposed to have perfect quality. NR measures attempt to assess the quality of an image without any reference to the original.

A Very Fast and Accurate Image Quality Assessment Method based on Mean Squared Error with Difference of Gaussians

2020 ◽  
Vol 64 (1) ◽  
pp. 10502-1-10502-5
Author(s):  
Sung-Ho Bae ◽  
Seong-Bae Park
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Mean Squared Error ◽  
Visual Quality ◽  
Prediction Performance ◽  
Difference Of Gaussians ◽  
Mathematical Properties ◽  
Squared Error ◽  
Computational Simplicity

Abstract Mean squared error (MSE) has long been the most useful objective image quality assessment (IQA) metric due to its mathematical tractability and computational simplicity, although it has shown poor correlations with the perceived visual quality for distorted images. Contrary to the MSE, recent IQA methods are more closely related with measured visual quality. However, their applications are somewhat limited due to their heavy computational costs and inapplicability in optimization process. In order to develop a better IQA method that will be closer to the perceived visual quality, the authors aimed to incorporate simple yet powerful linear features into the form of MSE while retaining the advantages of computational simplicity and desirable mathematical properties of MSE. Through comprehensive experiments, the authors found that Difference of Gaussians (DoG) kernel significantly improves the prediction performance while keeping the aforementioned advantages in the form of MSE. The proposed method performs better as the DoG filtering well approximates the behaviors of neural response functions in the visual cortex of the human visual system, thus extracting perceptually important features. At the same time, it holds the computational simplicity and mathematical properties of MSE since DoG is a very simple linear kernel. Their extensive experiments showed that the proposed method provides competitive prediction performance to the recent IQA methods with a significantly lower computational complexity.

scholarly journals On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

2018 ◽  
Vol 4 (10) ◽  
pp. 114 ◽  
Author(s):  
Pedro Garcia Freitas ◽  
Luísa da Eira ◽  
Samuel Santos ◽  
Mylene Farias
Keyword(s):  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Local Binary Patterns ◽  
Reference Image ◽  
Fundamental Interest ◽  
Wide Range ◽  
Estimate Quality

Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.

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