Arrow’s Impossibility Theorem inspired subjective image quality assessment approach

Zhu, Wenhan; Zhai, Guangtao; Hu, Menghan; Liu, Jing; Yang, Xiaokang

doi:10.1016/j.sigpro.2017.12.001

Cited by 13 publications

(2 citation statements)

References 33 publications

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“…The illuminance of the experimental environment keeps low for comfortable and distinct view. Since this experiment requires knowledge of the field of image processing and careful observation, we recruit three experts in image processing and television program production to assess the quality of these test materials [19]. The one hundred graded scale is employed in our experiment.…”

Section: True and Pseudo 4k Images Databasementioning

confidence: 99%

Perceptual Quality Assessment for Recognizing True and Pseudo 4k Content

Zhu

Zhai

Min

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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To meet the imperative demand for monitoring the quality of Ultra High-Definition (UHD) content in multimedia industries, we propose an efficient no-reference (NR) image quality assessment (IQA) metric to distinguish original and pseudo 4K contents and measure the quality of their quality in this paper. First, we establish a database including more than 3000 4K images composed of natural 4K images together with upscaled versions interpolated from 1080p and 720p images by fourteen algorithms. To improve computing efficiency, our model segments the input image and selects three representative patches by local variances. Then, we extract the histogram features and cut-off frequency features in the frequency domain as well as the natural scenes statistic (NSS) based features from the representative patches. Finally, we employ support vector regressor (SVR) to aggregate these extracted features as an overall quality metric to predict the quality score of the target image. Extensive experimental comparisons using seven common evaluation indicators demonstrate that the proposed model outperforms the competitive NR IQA methods and has a great ability to distinguish true and pseudo 4K images.

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Section: True and Pseudo 4k Images Databasementioning

confidence: 99%

Perceptual Quality Assessment for Recognizing True and Pseudo 4k Content

Zhu

Zhai

Min

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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show abstract

“…Therefore, it is essential to develop reliable automatic image quality assessment (IQA) measures for the evaluation of results of image processing methods for the acquisition, storage, transmission, restoration, or enhancement. The main role of IQA measures is to provide the objective assessment and replace cumbersome tests with human subjects [4]. The IQA measures are divided into three categories, based on the availability of reference images [1,5,6]: full-reference (FR), reduced-reference (RR), and no-reference (NR) approaches.…”

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confidence: 99%

No-Reference Image Quality Assessment with Local Gradient Orientations

Oszust

2019

Symmetry

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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.Symmetry 2019, 11, 95 2 of 20 patches [28] can be found in the literature.Since the supervised learning bridges image statistics with the perceptual quality, it is often applied to obtain a model used for the quality prediction. For the learning, the Support Vector Regression (SVR) [20,21,23,27], neural networks [24], or random forests [29] are applied. In methods that do not use supervised learning, distortion types are modeled with a set of centroids of quality levels or NSS from multiple cues [30,31]. Another direction is to employ a pseudo-reference image which is created and compared with a distorted image with blockiness, sharpness, and noisiness metrics [32]. Recently, many NR-IQA approaches which use deep neural network (DNN) architectures have been introduced. They merge the feature extraction and quality prediction steps. However, they suffer from a small number of training examples available in IQA benchmark datasets or use complex architectures that are devoted to image recognition tasks. To overcome these limitations, most of them use image patches [33,34], train models using FR-IQA measures instead of subjective scores [34,35], or perform fine-tuning to adopt an architecture to the IQA [36]. Interestingly, some DNN-based approaches use features introduced in earlier methods [35].The HVS is sensitive to local structures, which are often described using local binary patterns (LBP) and gradient-based statistics. However, a spatial distribution of LBP may not be able to capture more complex structures [37]. Thus, statistics extracted from gradient maps often occur in conjunction with other approaches to improve the IQA performance [27,38]. Such techniques use global distributions of gradient magnitude maps [25], relative gradient orientations or magnitude [24,39].To describe an image and efficiently take into account local gradient orientations, Histogram of Oriented Gradients (HOG) descriptor can be used [40]. However, the HOG produces high-dimensional feature vectors, which are devoted to object recognition...

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Comparative Sharpness Evaluation for Mobile Phone Photos

Zhai

Zhu

et al. 2021

Lecture Notes in Computer Science

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Arrow’s Impossibility Theorem inspired subjective image quality assessment approach

Cited by 13 publications

References 33 publications

Perceptual Quality Assessment for Recognizing True and Pseudo 4k Content

Perceptual Quality Assessment for Recognizing True and Pseudo 4k Content

No-Reference Image Quality Assessment with Local Gradient Orientations

Comparative Sharpness Evaluation for Mobile Phone Photos

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