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"Quality assessment"
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Perceptual image quality assessment: a survey
Perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display. Quality assessment for visual signals can be performed subjectively and objectively, and objective quality assessment is usually preferred owing to its high efficiency and easy deployment. A large number of subjective and objective visual quality assessment studies have been conducted during recent years. In this survey, we give an up-to-date and comprehensive review of these studies. Specifically, the frequently used subjective image quality assessment databases are first reviewed, as they serve as the validation set for the objective measures. Second, the objective image quality assessment measures are classified and reviewed according to the applications and the methodologies utilized in the quality measures. Third, the performances of the state-of-the-art quality measures for visual signals are compared with an introduction of the evaluation protocols. This survey provides a general overview of classical algorithms and recent progresses in the field of perceptual image quality assessment.
Journal Article
Perceptual video quality assessment: a survey
Perceptual video quality assessment plays a vital role in the field of video processing due to the existence of quality degradations introduced in various stages of video signal acquisition, compression, transmission and display. With the advancement of Internet communication and cloud service technology, video content and traffic are growing exponentially, which further emphasizes the requirement for accurate and rapid assessment of video quality. Therefore, numerous subjective and objective video quality assessment studies have been conducted over the past two decades for both generic videos and specific videos such as streaming, user-generated content, 3D, virtual and augmented reality, high dynamic range, high frame rate, audio-visual, etc. This survey provides an up-to-date and comprehensive review of these video quality assessment studies. Specifically, we first review the subjective video quality assessment methodologies and databases, which are necessary for validating the performance of video quality metrics. Second, the objective video quality assessment measures for general purposes are categorized and surveyed according to the methodologies utilized in the quality measures. Third, we overview the objective video quality assessment measures for specific applications and emerging topics. Finally, the performance of the state-of-the-art video quality assessment measures is compared and analyzed. This survey provides a systematic overview of both classical works and recent progress in the realm of video quality assessment, which can help other researchers quickly access the field and conduct relevant research.
Journal Article
Blind Image Quality Assessment: Exploring Content Fidelity Perceptibility via Quality Adversarial Learning
In deep learning-based no-reference image quality assessment (NR-IQA) methods, the absence of reference images limits their ability to assess content fidelity, making it difficult to distinguish between original content and distortions that degrade quality. To address this issue, we propose a quality adversarial learning framework emphasizing both content fidelity and prediction accuracy. The main contributions of this study are as follows: First, we investigate the importance of content fidelity, especially in no-reference scenarios. Second, we propose a quality adversarial learning framework that dynamically adapts and refines the image quality assessment process on the basis of the quality optimization results. The framework generates adversarial samples for the quality prediction model, and simultaneously, the quality prediction model optimizes the quality prediction model by using these adversarial samples to maintain fidelity and improve accuracy. Finally, we demonstrate that by employing the quality prediction model as a loss function for image quality optimization, our framework effectively reduces the generation of artifacts, highlighting its superior ability to preserve content fidelity. The experimental results demonstrate the validity of our method compared with state-of-the-art NR-IQA methods. The code is publicly available at the following website:
https://github.com/Land5cape/QAL-IQA.
Journal Article
Hierarchical Curriculum Learning for No-Reference Image Quality Assessment
Despite remarkable success has been achieved by convolutional neural networks (CNNs) in no-reference image quality assessment (NR-IQA), there still exist many challenges in improving the performance of IQA for authentically distorted images. An important factor is that the insufficient annotated data limits the training of high-capacity CNNs to accommodate diverse distortions, complicated semantic structures and high-variance quality scores of these images. To address this problem, this paper proposes a hierarchical curriculum learning (HCL) framework for NR-IQA. The main idea of the proposed framework is to leverage the external data to learn the prior knowledge about IQA widely and progressively. Specifically, as a closely-related task with NR-IQA, image restoration is used as the first curriculum to learn the image quality related knowledge (i.e., semantic and distortion information) on massive distorted-reference image pairs. Then multiple lightweight subnetworks are designed to learn human scoring rules on multiple available synthetic IQA datasets independently, and a cross-dataset quality assessment correlation (CQAC) module is proposed to fully explore the similarities and diversities of different scoring rules. Finally, the whole model is fine-tuned on the target authentic IQA dataset to fuse the learned knowledge and adapt to the target data distribution. Experimental results show that our model achieves state-of-the-art performance on multiple standard authentic IQA datasets. Moreover, the generalization of our model is fully validated by the cross-dataset evaluation and the gMAD competition. In addition, extensive analyses prove that the proposed HCL framework is effective in improving the performance of our model.
Journal Article
Comprehensive understanding of groundwater quality for domestic and agricultural purposes in terms of health risks in a coal mine area of the Ordos basin, north of the Chinese Loess Plateau
Assessment of groundwater quality and health risk was conducted in the Shenfu coal mine area in Ordos basin, northwestern China. Statistical analysis, Piper and Chadha diagrams were used to reveal the hydrogeochemical characteristics of groundwater via physicochemical analysis of 44 collected samples. The suitability of groundwater was assessed for domestic and irrigation purposes, and the fuzzy comprehensive method was adopted to assess the overall groundwater quality for further discussion on groundwater management. The model recommended by the USEPA was selected to estimate the non-carcinogenic risks caused by NO3−, NO2−, NH4+, F−, Fe and Mn through oral ingestion and direct dermal contact. The results revealed that the predominant hydrochemical types of groundwater were SO4∙Cl–Ca∙Mg and HCO3–Ca∙Mg types and the major cations and anions followed the orders of Ca2+ > Na+ > Mg2+ >K+ and HCO3− > SO42− > Cl−, respectively. Groundwater is generally acceptable for irrigation. However, for domestic purposes, 47.73% of the collected samples are of excellent and good quality and are suitable for direct consumption. Both adults and children face non-carcinogenic risks because of exposure to contaminants such as nitrate, nitrite and fluoride. The risk to children is higher than that to adults, which is consistent with other studies. Nitrite contributes most to the risks, followed by nitrate and fluoride. Home-use water quality improvement devices and rainwater harvesting are suggested to enhance the groundwater quality protection and management in this area. The research also indicates that health risk assessment should always accompany general water quality assessment to ensure the reliability of the water quality assessment.
Journal Article
Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments. Perceptual datasets gathered for this purpose have provided useful benchmarks for improving IQA methods, but their heavy use creates a risk of overfitting. Here, we perform a large-scale comparison of IQA models in terms of their use as objectives for the optimization of image processing algorithms. Specifically, we use eleven full-reference IQA models to train deep neural networks for four low-level vision tasks: denoising, deblurring, super-resolution, and compression. Subjective testing on the optimized images allows us to rank the competing models in terms of their perceptual performance, elucidate their relative advantages and disadvantages in these tasks, and propose a set of desirable properties for incorporation into future IQA models.
Journal Article