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

Varga, Domonkos

doi:10.3390/jimaging6080075

Cited by 25 publications

(31 citation statements)

References 48 publications

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“…We focus our research by combining biological theories, and the distinction of energy entropy included in different stimuli (i.e., distortions) is huge, which may cause different distortions to be processed in different visual cortexes. This presumption is indeed verified by some studies—that there exists an approximately linear relationship between energy entropy and a visual attention mechanism [ 45 , 46 ]. Thus, a two-component framework for visual attention mechanism stimulated by stimuli energy entropy (short for visual entropy) was proposed to simulate the physiological structure of the human brain processing visual information [ 47 ].…”

Section: Motivationsupporting

confidence: 54%

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

Jin

Peng

Zou

et al. 2021

Entropy

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Multiview video plus depth is one of the mainstream representations of 3D scenes in emerging free viewpoint video, which generates virtual 3D synthesized images through a depth-image-based-rendering (DIBR) technique. However, the inaccuracy of depth maps and imperfect DIBR techniques result in different geometric distortions that seriously deteriorate the users’ visual perception. An effective 3D synthesized image quality assessment (IQA) metric can simulate human visual perception and determine the application feasibility of the synthesized content. In this paper, a no-reference IQA metric based on visual-entropy-guided multi-layer features analysis for 3D synthesized images is proposed. According to the energy entropy, the geometric distortions are divided into two visual attention layers, namely, bottom-up layer and top-down layer. The feature of salient distortion is measured by regional proportion plus transition threshold on a bottom-up layer. In parallel, the key distribution regions of insignificant geometric distortion are extracted by a relative total variation model, and the features of these distortions are measured by the interaction of decentralized attention and concentrated attention on top-down layers. By integrating the features of both bottom-up and top-down layers, a more visually perceptive quality evaluation model is built. Experimental results show that the proposed method is superior to the state-of-the-art in assessing the quality of 3D synthesized images.

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Section: Motivationsupporting

confidence: 54%

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

Jin

Peng

Zou

et al. 2021

Entropy

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“…In this study, an NR-IQA method is presented which relies on a novel feature vector containing a set of quality-aware features that globally characterizes the statistics of a given input image to be assessed. Specifically, the proposed feature vector partially improves further our previous work [16]. A set of shape descriptors is proposed to the local fractal dimension distribution and first digit distribution feature vectors to capture better image distortions.…”

Section: Contributionsmentioning

confidence: 88%

“…In contrast, Li et al [ 15 ] proposed a gradient weighted histogram of local binary patterns for quality aware features. In [ 16 ], a set of quality aware statistical features (first digit distribution in the gradient magnitude and wavelet domain, color statistics) were combined with powerful perceptual features (colorfulness, global contrast factor, entropy, etc.) to train an Gaussian process regression (GPR) algorithm for quality prediction.…”

Section: Introductionmentioning

confidence: 99%

“…A set of shape descriptors is proposed to the local fractal dimension distribution and first digit distribution feature vectors to capture better image distortions. Moreover, we point out that besides the wavelet coefficients [ 16 ], discrete cosine transform coefficients and singular values of an image are also suitable to derive first digit distribution features based on Benford’s law. Motivated by the model of extended classical receptive field (ECRF), Bilaplacian quality-aware features are also incorporated into the introduced model.…”

Section: Introductionmentioning

confidence: 99%

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No-Reference Image Quality Assessment with Global Statistical Features

Varga

2021

J. Imaging

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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.

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“…Specifically, the framework is divided into two stages: In this section, the six different aspects of feature extraction are in detailed description first: complexity, contrast, sharpness, brightness, colorfulness, and naturalness. These quality perception features that affect image quality are widely applied in NR-IQA [25][26][27] and have yielded good results. Second, the integrated SVR technology in the regression module is mainly explained.…”

Section: Objective Quality Assessment Of Display Productsmentioning

confidence: 99%

Subjective and Objective Quality Assessments of Display Products

Zhang

et al. 2021

Entropy

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In recent years, people’s daily lives have become inseparable from a variety of electronic devices, especially mobile phones, which have undoubtedly become necessity in people’s daily lives. In this paper, we are looking for a reliable way to acquire visual quality of the display product so that we can improve the user’s experience with the display product. This paper proposes two major contributions: the first one is the establishment of a new subjective assessment database (DPQAD) of display products’ screen images. Specifically, we invited 57 inexperienced observers to rate 150 screen images showing the display product. At the same time, in order to improve the reliability of screen display quality score, we combined the single stimulation method with the stimulation comparison method to evaluate the newly created display products’ screen images database effectively. The second one is the development of a new no-reference image quality assessment (IQA) metric. For a given image of the display product, first our method extracts 27 features by analyzing the contrast, sharpness, brightness, etc., and then uses the regression module to obtain the visual quality score. Comprehensive experiments show that our method can evaluate natural scene images and screen content images at the same time. Moreover, compared with ten state-of-the-art IQA methods, our method shows obvious superiority on DPQAD.

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

Cited by 25 publications

References 48 publications

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

No-Reference Image Quality Assessment with Global Statistical Features

Subjective and Objective Quality Assessments of Display Products

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