scholarly journals No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

2021 ◽  
Vol 7 (7) ◽  
pp. 112
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Multiple Scales ◽  
Digital Images ◽  
Input Image ◽  
Perceptual Quality ◽  
Reference Image ◽  
Benchmark Datasets ◽  
In The Wild

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).

scholarly journals Multi-Pooled Inception Features for No-Reference Image Quality Assessment

2020 ◽  
Vol 10 (6) ◽  
pp. 2186
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Input Image ◽  
Reference Image ◽  
Parameter Study ◽  
Image Size ◽  
In The Wild ◽  
Display Technology ◽  
Multi Level

Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.

scholarly journals No-Reference Image Quality Assessment with Global Statistical Features

2021 ◽  
Vol 7 (2) ◽  
pp. 29
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Perceptual Quality ◽  
Reference Image ◽  
Distribution Features ◽  
Quality Assessment Method ◽  
In The Wild ◽  
Time Systems

The perceptual quality of digital images is often deteriorated during storage, compression, and transmission. The most reliable way of assessing image quality is to ask people to provide their opinions on a number of test images. However, this is an expensive and time-consuming process which cannot be applied in real-time systems. In this study, a novel no-reference image quality assessment method is proposed. The introduced method uses a set of novel quality-aware features which globally characterizes the statistics of a given test image, such as extended local fractal dimension distribution feature, extended first digit distribution features using different domains, Bilaplacian features, image moments, and a wide variety of perceptual features. Experimental results are demonstrated on five publicly available benchmark image quality assessment databases: CSIQ, MDID, KADID-10k, LIVE In the Wild, and KonIQ-10k.

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 Quality Assessment on Authentically Distorted Images by Expanding Proxy Labels

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 252 ◽  
Author(s):  
Xiaodi Guan ◽  
Fan Li ◽  
Lijun He
Keyword(s):  
Image Quality ◽  
Visual Perception ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Image Features ◽  
Quality Score ◽  
Fine Tuning ◽  
Reference Image ◽  
Human Visual Perception ◽  
In The Wild

In this paper, we propose a no-reference image quality assessment (NR-IQA) approach towards authentically distorted images, based on expanding proxy labels. In order to distinguish from the human labels, we define the quality score, which is generated by using a traditional NR-IQA algorithm, as “proxy labels”. “Proxy” means that the objective results are obtained by computer after the extraction and assessment of the image features, instead of human judging. To solve the problem of limited image quality assessment (IQA) dataset size, we adopt a cascading transfer-learning method. First, we obtain large numbers of proxy labels which denote the quality score of authentically distorted images by using a traditional no-reference IQA method. Then the deep network is trained by the proxy labels, in order to learn IQA-related knowledge from the amounts of images with their scores. Ultimately, we use fine-tuning to inherit knowledge represented in the trained network. During the procedure, the mapping relationship fits in with human visual perception closer. The experimental results demonstrate that the proposed algorithm shows an outstanding performance as compared with the existing algorithms. On the LIVE In the Wild Image Quality Challenge database and KonIQ-10k database (two standard databases for authentically distorted image quality assessment), the algorithm realized good consistency between human visual perception and the predicted quality score of authentically distorted images.

scholarly journals IE-IQA: Intelligibility Enriched Generalizable No-Reference Image Quality Assessment

2021 ◽  
Vol 15 ◽  
Author(s):  
Tianshu Song ◽  
Leida Li ◽  
Hancheng Zhu ◽  
Jiansheng Qian
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Selection Strategy ◽  
Reference Image ◽  
Quality Model ◽  
Generalization Ability ◽  
Urgent Task ◽  
Generalization Problem ◽  
In The Wild

Image quality assessment (IQA) for authentic distortions in the wild is challenging. Though current IQA metrics have achieved decent performance for synthetic distortions, they still cannot be satisfactorily applied to realistic distortions because of the generalization problem. Improving generalization ability is an urgent task to make IQA algorithms serviceable in real-world applications, while relevant research is still rare. Fundamentally, image quality is determined by both distortion degree and intelligibility. However, current IQA metrics mostly focus on the distortion aspect and do not fully investigate the intelligibility, which is crucial for achieving robust quality estimation. Motivated by this, this paper presents a new framework for building highly generalizable image quality model by integrating the intelligibility. We first analyze the relation between intelligibility and image quality. Then we propose a bilateral network to integrate the above two aspects of image quality. During the fusion process, feature selection strategy is further devised to avoid negative transfer. The framework not only catches the conventional distortion features but also integrates intelligibility features properly, based on which a highly generalizable no-reference image quality model is achieved. Extensive experiments are conducted based on five intelligibility tasks, and the results demonstrate that the proposed approach outperforms the state-of-the-art metrics, and the intelligibility task consistently improves metric performance and generalization ability.

PENILAIAN KUALITI IMEJ DIGITAL BERDASARKAN KAEDAH CIRI-CIRI SISTEM PENGLIHATAN MANUSIA DAN PRINSIP STRUKTUR IMEJ

2016 ◽  
Vol 78 (5-10) ◽  
Author(s):  
Bahbibi Rahmatullah ◽  
Siti Tasnim Mahamud
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Visual Information ◽  
Image Quality Assessment ◽  
Digital Images ◽  
Reference Method ◽  
Assessment Method ◽  
Reference Image ◽  
Human Observer ◽  
Full Reference

Tremendous advances of information technology provide a large role for digital images for delivering information quickly and accurately. However, digital images are exposed to distortions and imperfect quality during acquisition, compression, transmission, processing and reproduction. Therefore, the development of effectively image quality assessment (IQA) is crucial in order to identify and measure the distortion in image quality. Perception by human observers (manually) as the ultimate receiver of the visual information contained in an image and most reliable to assess the quality of image. However, manual subjective assessment method is considered costly and time consuming. This lead to the development of proposed automatic method to measure image quality as accurately as the manual method. The goal of objective image quality assessment is to develop a computational model that can accurately and automatically predict the perceptual image quality. An ideal objective IQA method should be able to imitate the quality predictions of an average human observer. Full-reference image quality assessment is a method where image with perfect quality provided as a reference image for guiding the IQA system. This paper presents the study and comparison between two full-reference method that frequently used in IQA system that is method based on the properties of human visual system (HVS) and method based on principle of image structure. Both of this method is proven can be used to measure digital images quality accurately and depends on distortion types that occurred on measured images.

scholarly journals No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

2021 ◽  
Vol 12 (1) ◽  
pp. 101
Author(s):  
Domonkos Varga
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
Image Quality Assessment ◽  
Digital Images ◽  
Decision Fusion ◽  
Reference Image ◽  
Different Types

No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyday life is continuously growing. Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment. Since deep learning relies on a massive amount of labeled data, utilizing pretrained networks has become very popular in the literature. In this study, we introduce a novel, deep learning-based NR-IQA architecture that relies on the decision fusion of multiple image quality scores coming from different types of convolutional neural networks. The main idea behind this scheme is that a diverse set of different types of networks is able to better characterize authentic image distortions than a single network. The experimental results show that our method can effectively estimate perceptual image quality on four large IQA benchmark databases containing either authentic or artificial distortions. These results are also confirmed in significance and cross database tests.

scholarly journals Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 220 ◽  
Author(s):  
Rafał Obuchowicz ◽  
Mariusz Oszust ◽  
Marzena Bielecka ◽  
Andrzej Bielecki ◽  
Adam Piórkowski
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Correlation Coefficients ◽  
Input Image ◽  
Perceptual Quality ◽  
Intensity Difference ◽  
Mr Images ◽  
Local Intensity

An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.

scholarly journals No-Reference Image Quality Assessment with Local Gradient Orientations

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 95 ◽  
Author(s):  
Mariusz Oszust
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Support Vector ◽  
Human Observer ◽  
Learning Approaches ◽  
Benchmark Datasets ◽  
Highly Correlated ◽  
Local Gradient

Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are processed, as well as their high-order derivatives. Finally, statistics of used perceptual features are mapped to subjective scores by the support vector regression (SVR) technique. The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.

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