A technical review of no-reference image quality assessment algorithms for contrast distorted images

Automatic image quality assessment similar to human vision perception is an essential process for real-time image processing applications to perform perceptual image assessments for effectively achieving their goals. As no-reference image quality assessment (NR-IQA) schemes perform perceptual assessments of images without any information about their original version, these algorithms suit real-time computer vision techniques because of the non-availability of reference images. Contrast and colorfulness play important roles in determining the quality of color images. By combining many IQA metrics, a number of combined metrics had been devised. This study provides an insight into major NR-IQA methods and their effectiveness in assessing contrast, colorfulness, and overall quality of contrast-degraded images with technical analysis. The effectiveness of top-ranking NR-IQA methods is experimentally assessed with benchmark assessment methods on images from benchmarked databases. The study provides insight into open research challenges in the area of NR-IQA for developing new promising methods by clearly demarcating the difficulties of top-ranking NR-IQA methods.

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No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Journal of Imaging ◽

10.3390/jimaging6080075 ◽

2020 ◽

Vol 6 (8) ◽

pp. 75

Author(s):

Domonkos Varga

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Reference Image ◽

Phase Congruency ◽

Perceptual Features ◽

Proposed Model ◽

Dark Channel ◽

Reference Images ◽

Spatial Domains

The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.

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

Sensors ◽

10.3390/s20226457 ◽

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.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-949-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 949-954 ◽

Cited By ~ 1

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.

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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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Center-Emphasized Visual Saliency and a Contrast-Based Full Reference Image Quality Index

Symmetry ◽

10.3390/sym11030296 ◽

2019 ◽

Vol 11 (3) ◽

pp. 296 ◽

Cited By ~ 2

Author(s):

Md. Layek ◽

A. Uddin ◽

Tuyen Le ◽

TaeChoong Chung ◽

Eui-Nam Huh

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Large Scale ◽

Image Quality Assessment ◽

Visual Saliency ◽

Human Vision ◽

Reference Image ◽

Full Reference ◽

Human Attention ◽

Similarity Maps

Objective image quality assessment (IQA) is imperative in the current multimedia-intensive world, in order to assess the visual quality of an image at close to a human level of ability. Many parameters such as color intensity, structure, sharpness, contrast, presence of an object, etc., draw human attention to an image. Psychological vision research suggests that human vision is biased to the center area of an image and display screen. As a result, if the center part contains any visually salient information, it draws human attention even more and any distortion in that part will be better perceived than other parts. To the best of our knowledge, previous IQA methods have not considered this fact. In this paper, we propose a full reference image quality assessment (FR-IQA) approach using visual saliency and contrast; however, we give extra attention to the center by increasing the sensitivity of the similarity maps in that region. We evaluated our method on three large-scale popular benchmark databases used by most of the current IQA researchers (TID2008, CSIQ and LIVE), having a total of 3345 distorted images with 28 different kinds of distortions. Our method is compared with 13 state-of-the-art approaches. This comparison reveals the stronger correlation of our method with human-evaluated values. The prediction-of-quality score is consistent for distortion specific as well as distortion independent cases. Moreover, faster processing makes it applicable to any real-time application. The MATLAB code is publicly available to test the algorithm and can be found online at.

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

Image Analysis & Stereology ◽

10.5566/ias.1534 ◽

2018 ◽

Vol 37 (2) ◽

pp. 105 ◽

Cited By ~ 1

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.

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Analysis and Evaluation of Image Fusion Algorithms

SAMRIDDHI A Journal of Physical Sciences Engineering and Technology ◽

10.18090/samriddhi.v6i2.1564 ◽

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.

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A New Image Quality Assessment Method Based on SSIM and TV Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.65.542 ◽

2011 ◽

Vol 65 ◽

pp. 542-550

Author(s):

Lu Lu Pang ◽

Cong Li Li ◽

De Ning Qi ◽

Tao Zou

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Explicit Knowledge ◽

Image Quality Assessment ◽

Structural Similarity ◽

Assessment Method ◽

Reference Image ◽

Distorted Image ◽

Quality Assessment Method ◽

Reference Images

In this paper, a new image quality assessment method has been proposed in which can judge the quality of images without explicit knowledge of the reference images ,it is based on the SSIM(Structural Similarity) and TV(total variation) model. Firstly, add noises to distorted image to quantitatively determine, it can get the degraded image; secondly, use the improved self-adaptive gradient weights of the TV algorithms to denoising the distorted image, it can get the “fake” reference image, then use the classical SSIM methods to make reference evaluation between the distorted image and the “fake” reference image, after modified, the results is the no reference evaluating indicator. The experiment separated use the standard testing images and the degraded images from the LIVE database to make evaluate experiment, the result show that it is consistent to the result of MOS. This method is no need of reference images, it can use widely.

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

Journal of Imaging ◽

10.3390/jimaging4100114 ◽

2018 ◽

Vol 4 (10) ◽

pp. 114 ◽

Cited By ~ 4

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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