Quality Assessment Algorithms Research Articles

Blind Quality Assessment of Dense 3D Point Clouds with Structure Guided Resampling

Objective quality assessment of 3D point clouds is essential for the development of immersive multimedia systems in real-world applications. Despite the success of perceptual quality evaluation for 2D images and videos, blind/no-reference metrics are still scarce for 3D point clouds with large-scale irregularly distributed 3D points. Therefore, in this paper, we propose an objective point cloud quality index with Structure Guided Resampling (SGR) to automatically evaluate the perceptually visual quality of dense 3D point clouds. The proposed SGR is a general-purpose blind quality assessment method without the assistance of any reference information. Specifically, considering that the human visual system (HVS) is highly sensitive to structure information, we first exploit the unique normal vectors of point clouds to execute regional pre-processing which consists of keypoint resampling and local region construction. Then, we extract three groups of quality-related features, including: 1) geometry density features; 2) color naturalness features; 3) angular consistency features. Both the cognitive peculiarities of the human brain and naturalness regularity are involved in the designed quality-aware features that can capture the most vital aspects of distorted 3D point clouds. Extensive experiments on several publicly available subjective point cloud quality databases validate that our proposed SGR can compete with state-of-the-art full-reference, reduced-reference, and no-reference quality assessment algorithms.

HDRC: a subjective quality assessment database for compressed high dynamic range image

AbstractEvaluating the quality of high dynamic range (HDR) images has emerged as a challenging and contemporary topic with the proliferation of HDR content. In this work, a new HDR compression (HDRC) database is proposed, aiming to provide a benchmark for the development of full-reference HDR image quality assessment (IQA) algorithms when facing the latest HDR compression distortions. In particular, the proposed HDRC database is the first HDR-IQA database to incorporate Versatile Video Coding (VVC) compression distortions, closely associated with practical application scenarios. Furthermore, the proposed HDRC database is currently the largest HDR-IQA database, including 80 reference images and 400 distorted images. Extensive experiments are conducted by studying the performance compared to existing HDR-IQA databases when evaluating three HDR-specific IQA models and nine IQA models prevalent for low dynamic range (LDR) content, revealing the challenges the proposed HDRC database brings. The results indicate that the existing IQA models demonstrate noticeable decreases in accuracy when assessing new compression distortions, underscoring the need for the development of novel HDR-IQA models. Consequently, the suggested HDRC database can serve as a potential database for HDR-IQA research, fostering a comprehensive exploration of the associated fields.

No-Reference Image Quality Assessment Algorithm

The presented article focuses on the development of a local image quality assessment algorithm, specifically designed for analyzing contrast and overall visual data quality. The proposed algorithm aims to enhance the efficiency of image assessment, particularly in conditions of low contrast and the presence of various types of noise. The algorithm's methodology takes into account spectral ranges and provides precise local contrast assessment, making it applicable to a broad spectrum of tasks related to image analysis and enhancement. The developed approach has the potential to improve the quality of visual data by supporting crucial aspects of contrast perception and overall image quality.

McmIQA: Multi-Module Collaborative Model for No-Reference Image Quality Assessment

No reference image quality assessment is a technique that uses computers to simulate the human visual system and automatically evaluate the perceived quality of images. In recent years, with the widespread success of deep learning in the field of computer vision, many end-to-end image quality assessment algorithms based on deep learning have emerged. However, unlike other computer vision tasks that focus on image content, an excellent image quality assessment model should simultaneously consider distortions in the image and comprehensively evaluate their relationships. Motivated by this, we propose a Multi-module Collaborative Model for Image Quality Assessment (McmIQA). The image quality assessment is divided into three subtasks: distortion perception, content recognition, and correlation mapping. And specific modules are constructed for each subtask: the distortion perception module, the content recognition module, and the correlation mapping module. Specifically, we apply two contrastive learning frameworks on two constructed datasets to train the distortion perception module and the content recognition module to extract two types of features from the image. Subsequently, using these extracted features as input, we employ a ranking loss to train the correlation mapping module to predict image quality on image quality assessment datasets. Extensive experiments conducted on seven relevant datasets demonstrated that the proposed method achieves state-of-the-art performance in both synthetic distortion and natural distortion image quality assessment tasks.

Research on Oral English Learning System Integrating AI Speech Data Recognition and Speech Quality Evaluation Algorithm

The application of AI speech data recognition (SDR) and speech quality assessment algorithms in spoken English learning systems can help to realize the intelligence and automation of the process related to spoken English learning. To better assist the people concerned with the learning of spoken English, it is necessary to integrate AI speech data recognition technology in the process of learning spoken English to bring learners a learning environment and learning conditions that are not bound by time and space. As an important way to realize the natural communication of information between machines and people, AI speech recognition technology has significant application advantages and application values in eliminating the influence of the native language of English speaking learners and innovating the learning mode in the local area. In addition, the assessment of the reliability of spoken English language learning can be carried out in several dimensions. However, it is still difficult to unify the criteria of many sizes, bringing new application scenarios for AI SDR. Based on this, this paper first analyzes the theoretical basis of AI SDR. Then it investigates the speech data recognition method of the fused AI English-speaking learning mechanism and the process of fused AI English-speaking speech quality assessment. Finally, it designs and verifies the functions of the spoken English learning mechanism incorporating AI speech data identification and quality assessment. 

Leveraging K-Nearest Neighbors for Enhanced Fruit Classification and Quality Assessment

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for fruit classification and quality assessment, aiming to enhance agricultural practices through machine learning. Employing a comprehensive dataset that encapsulates various fruit attributes such as size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality, the research leverages a 5-fold cross-validation method to ensure the reliability and generalizability of the KNN model's performance. The findings reveal that the KNN algorithm demonstrates high accuracy, precision, recall, and F1-Score across all metrics, indicating its efficacy in classifying fruits and predicting their quality accurately. These results not only validate the algorithm's potential in agricultural applications but also align with existing research on machine learning's capability to tackle complex classification problems. The study's discussions extend to the practical implications of implementing a KNN-based model in the agricultural sector, highlighting the possibility of revolutionizing quality control and inventory management processes. Moreover, the research contributes to the field by confirming the hypothesis regarding the effectiveness of KNN in agricultural settings and lays the foundation for future explorations that could integrate multiple machine learning techniques for enhanced outcomes. Recommendations for subsequent studies include expanding the dataset and exploring algorithmic synergies, aiming to further the advancements in agricultural technology and machine learning applications.

Adversarial attacks on video quality assessment models

Most currently developed video quality assessment (VQA) algorithms have achieved excellent performance by using deep neural network (DNN). However, DNN is vulnerable to adversarial attacks, as an efficient surrogate for validating the model robustness, and there lack adversarial attack methods against VQA models. To this end, we propose a spatiotemporal attack network to generate adversarial examples for evaluating the robustness of VQA models that contains a spatial subnetwork and a temporal subnetwork. The proposed network, dubbed the Space-Time Quality Attack Network (STQA-Net1), first computes the just noticeable difference (JND) maps of a video sequence as the input of the spatial subnetwork. The spatial subnetwork encodes the computed maps as spatial features and feeds the spatial features to the temporal subnetwork. Then, the spatial features are fused with the output of the temporal subnetwork and the fused features are decoded as attack weight maps. A visual constraint is used to control the visibility of perturbations and guide the generation of perturbation maps by multiplying JND maps with attack weight maps. Finally, the generated perturbation maps are added to the original video to form an adversarial example. Further, we also try to design a two-branch network to generate two opposite examples in a targeted attack scenario. The proposed attack methods against six state-of-the-art VQA algorithms are thoroughly tested on three VQA databases. The experimental results show that the proposed attack methods are very effective for testing the robustness of VQA models.

A Comparison Study of Edge Line Estimation Algorithms for Dimensional Quality Assessment of Precast Concrete Slabs

Point cloud data-based edge line extraction is an important task for accurate geometrical inspection of precast concrete (PC) elements in the construction industry. Although a few edge extraction algorithms have been developed so far based on point cloud data, little attention has been paid on which edge extraction algorithm performs the best in terms of edge estimation accuracy. To tackle the research gap, this study aims to evaluate currently available edge extraction algorithms in order to determine optimal algorithm for precise geometrical inspection of PC elements. To do this, simulated scan points are first generated and used for algorithm performance analysis using a geometrical model and a measurement noise modeling that determine the coordinates of simulated scan points. For validation of the simulation approach, comparison tests with experimental data are performed and the results show that the simulation approach has a high similarity of more than 90% compared to experimental data in terms of the number of scan points, scan pattern, and scan density, proving the effectiveness of the simulation-based evaluation method. In addition, it shows that a least square regression (LSR) algorithm provides the best performance with an edge extraction accuracy of 1.56 and 2.71 mm for simulated and experimental scan points, respectively. The contributions of this study are (1) development of the geometrical model and noise modeling based on actual scan data and (2) validation of simulated-based evaluation method on the lab-scale PC slabs.

How subjective CT image quality assessment becomes surprisingly reliable: pairwise comparisons instead of Likert scale.

The aim of this study is to improve the reliability of subjective IQ assessment using a pairwise comparison (PC) method instead of a Likert scale method in abdominal CT scans. Abdominal CT scans (single-center) were retrospectively selected between September 2019 and February 2020 in a prior study. Sample variance in IQ was obtained by adding artificial noise using dedicated reconstruction software, including reconstructions with filtered backprojection and varying iterative reconstruction strengths. Two datasets (each n = 50) were composed with either higher or lower IQ variation with the 25 original scans being part of both datasets. Using in-house developed software, six observers (five radiologists, one resident) rated both datasets via both the PC method (forcing observers to choose preferred scans out of pairs of scans resulting in a ranking) and a 5-point Likert scale. The PC method was optimized using a sorting algorithm to minimize necessary comparisons. The inter- and intraobserver agreements were assessed for both methods with the intraclass correlation coefficient (ICC). Twenty-five patients (mean age 61 years ± 15.5; 56% men) were evaluated. The ICC for interobserver agreement for the high-variation dataset increased from 0.665 (95%CI 0.396-0.814) to 0.785 (95%CI 0.676-0.867) when the PC method was used instead of a Likert scale. For the low-variation dataset, the ICC increased from 0.276 (95%CI 0.034-0.500) to 0.562 (95%CI 0.337-0.729). Intraobserver agreement increased for four out of six observers. The PC method is more reliable for subjective IQ assessment indicated by improved inter- and intraobserver agreement. This study shows that the pairwise comparison method is a more reliable method for subjective image quality assessment. Improved reliability is of key importance for optimization studies, validation of automatic image quality assessment algorithms, and training of AI algorithms. • Subjective assessment of diagnostic image quality via Likert scale has limited reliability. • A pairwise comparison method improves the inter- and intraobserver agreement. • The pairwise comparison method is more reliable for CT optimization studies.

An improved blind/referenceless image spatial quality evaluator algorithm for image quality assessment

An improved blind/referenceless image spatial quality evaluator algorithm for image quality assessment

A Study of Subjective and Objective Quality Assessment of HDR Videos.

As compared to standard dynamic range (SDR) videos, high dynamic range (HDR) content is able to represent and display much wider and more accurate ranges of brightness and color, leading to more engaging and enjoyable visual experiences. HDR also implies increases in data volume, further challenging existing limits on bandwidth consumption and on the quality of delivered content. Perceptual quality models are used to monitor and control the compression of streamed SDR content. A similar strategy should be useful for HDR content, yet there has been limited work on building HDR video quality assessment (VQA) algorithms. One reason for this is a scarcity of high-quality HDR VQA databases representative of contemporary HDR standards. Towards filling this gap, we created the first publicly available HDR VQA database dedicated to HDR10 videos, called the Laboratory for Image and Video Engineering (LIVE) HDR Database. It comprises 310 videos from 31 distinct source sequences processed by ten different compression and resolution combinations, simulating bitrate ladders used by the streaming industry. We used this data to conduct a subjective quality study, gathering more than 20,000 human quality judgments under two different illumination conditions. To demonstrate the usefulness of this new psychometric data resource, we also designed a new framework for creating HDR quality sensitive features, using a nonlinear transform to emphasize distortions occurring in spatial portions of videos that are enhanced by HDR, e.g., having darker blacks and brighter whites. We apply this new method, which we call HDRMAX, to modify the widely-deployed Video Multimethod Assessment Fusion (VMAF) model. We show that VMAF+HDRMAX provides significantly elevated performance on both HDR and SDR videos, exceeding prior state-of-the-art model performance. The database is now accessible at: https://live.ece.utexas.edu/research/LIVEHDR/LIVEHDR_index.html. The model will be made available at a later date at: https://live.ece.utexas.edu//research/Quality/index_algorithms.htm.

Loitering Based Human Crime Detection in Video Surveillance using Beluga Whale Adam Dingo Optimizer and Deep Convolutional Neural Network

Video Surveillance (VS) systems play a crucial role in maintaining security in public spaces, commercial establishments and residential areas. Detecting and preventing human-related crimes within the footage captured by these systems is a challenging task. Traditionally, VS systems rely on basic motion detection, which often leads to false alarms and inefficient use of resources. Loitering, a behavior frequently associated with criminal activities, requires more nuanced detection to reduce false positives and improve response times. Accurate tracking of individuals, especially in crowded environments, is another challenge. The chief objective of this research is to address these challenges by introducing an innovative Loitering-based Human Crime Detection (LHCD) module in VS. This module combines enhanced euclidean based Deep Simple Online Real-time Tracking (DSORT) with the Segmentation Quality Assessment (SQA) algorithm to accurately assess human travel distances. Also, this research integrates the Beluga Whale Adam Dingo Optimizer (BWADO) and a Deep Convolutional Neural Network (DCNN) to boost the precision and efficiency of Human Crime Detection (HCD) within loitering areas. The introduced approach demonstrates the effectiveness of introduced module, which reduces false alarms and enhances response times in VS. Outcomes demonstrate that the introduced approach outperforms existing approaches in various performance measures like accuracy (99.76%), F1-score (99.89%), recall (98.59%), precision (98.9%) and processing time (1.78s) demonstrating its superior effectiveness and potential for advancements in the field

Mathematical modeling of food production technological processes in a computer training complex for operator education

A computer training complex (CTC) structure has been designed for training food production operators, comprising interconnected subsystems generating output parameters. This CTC structure enables the configuration and storage of knowledge on specific technological processes, facilitating the customization of exercises by instructors. Individualized exercises are generated for students to perform in a virtual production environment, utilizing computer programs, tablets, smartphones, or simulated production equipment, including VR/AR technologies. During exercises, student actions are automatically recorded, and a quantitative assessment is provided based on standards, forming a comprehensive evaluation. A mathematical model, represented by a Markov transition graph, captures various states of the virtual production environment during simulated processes at the CTC. This model serves as the basis for constructing specific technological process models in the food industry, tailored to CTC hardware, software, and training methodologies. The methodology presented streamlines the development of training courses for CTC exercises, enhancing personnel knowledge and skills in food production. By utilizing these models, methods, and algorithms, CTCs can be tailored for training operators in diverse food production sectors. The approach outlined simplifies course creation, ensuring personnel development at the desired proficiency level through exercise quality assessment algorithms.

Dual-stream complex-valued convolutional network for authentic dehazed image quality assessment.

Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued convolutional neural network (CV-CNN) model to conduct automatic dehazed image quality evaluation. Specifically, a novel CV-CNN is employed that exploits the advantages of complex-valued representations, achieving better generalization capability on perceptual feature learning than real-valued ones. To learn more discriminative features to analyze the perceptual quality of dehazed images, we design a dual-stream CV-CNN architecture. The dual-stream model comprises a distortion-sensitive stream that operates on the dehazed RGB image, and a haze-aware stream on a novel dark channel difference image. The distortion-sensitive stream accounts for perceptual distortion artifacts, while the haze-aware stream addresses the possible presence of residual haze. Experimental results on three publicly available dehazed image quality assessment (DQA) databases demonstrate the effectiveness and generalization of our proposed CV-CNN DQA model as compared to state-of-the-art no-reference image quality assessment algorithms.

Considerations on the Evaluation of Biometric Quality Assessment Algorithms

Considerations on the Evaluation of Biometric Quality Assessment Algorithms

Методические основы оценки качества сайтов образовательных организаций (на примере Владивостокского государственного университета)

Currently, higher education institutions are focused on promoting their image on the Internet in order to occupy a certain niche there for presenting information about themselves. Thanks to the possibilities of the information Internet environment, universities contribute to improving the quality of the educational process. An important aspect in the formation of competitive advantages over other educational institutions is the quality of educational web resources. The distribution of scientific and educational streams is regulated by regulatory requirements that have a huge impact on the formation of the quality of websites of higher educational institutions. The important role of the monitoring process of assessing the quality of educational institutions' websites for the formation of recommendations based on the evaluation results according to the proposed methodology is revealed. The methodology is based on the theory of stakeholders, where groups of interested persons are considered by the fact of the existence of the studied object, indirectly or directly influencing it. A nomenclature of quality indicators has been developed based on the allocation of important criteria for each group, in this work these are students, parents, applicants and employees. The identification of indicators and values of their satisfaction serves as a foundation for the subsequent assessment of the quality of university websites according to the proposed methodology. The methodology includes several stages that accurately track all indicators during its use. The verification of the quality assessment algorithm was carried out on the example of the website of the Vladivostok State University. The data obtained are systematized and calculated according to the proposed formula, taking into account all interested groups. The results are of practical interest for a systematic assessment of the quality of educational institutions' websites and can be offered for use.

A brain-inspired quality assessment model for sonar image super-resolution

Sonar sensors are vital in the marine industry for detecting underwater targets in challenging conditions. The imaging distance and image resolution are negatively correlated due to the propagation characteristics of sound waves in water. Although Super-Resolution (SR) techniques alleviate this limitation, they introduce complex distortions that may not fit the desired utility of reconstructed sonar images. Quantifying image quality is essential, yet existing Image Quality Assessment (IQA) algorithms fail to simultaneously consider reconstruction distortions and the sonar image task background. Furthermore, the scarcity of sonar images poses challenges for deep-learning-based algorithms. To address these issues, we propose a brain-inspired model for Super-Resolution reconstructed Sonar Image Quality Assessment (SRSIQA) based on transfer learning. On the one hand, we adopt an effective feature extractor to extract multi-level features that align with the human brain recognition process. On the other hand, we develop an SEA-Block to implement feature weight adjustment and multi-level feature scale matching, given that some transferred features do not fit the IQA task well. Experimental results demonstrate the superiority of the proposed method.

New research on monaural speech segregation based on quality assessment

Speech enhancement (SE) is a pivotal technology in enhancing the quality and intelligibility of speech signals. Nevertheless, when processing speech signals under conditions of high signal-to-noise ratio (SNR), conventional SE techniques may inadvertently lead to a diminution in the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). This article introduces the innovative incorporation of the Non-Intrusive Speech Quality Assessment (NISQA) algorithm into SE systems. Through the comparison of pre and post-enhancement speech quality scores, it discerns whether the speech signal under consideration warrants enhancement processing, thereby mitigating potential deterioration in PESQ and STOI. Furthermore, this study delves into the ramifications of five prevalent speech features, namely, Mel Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), Relative Spectral Trans-formed Perceptual Linear Prediction coefficients (RASTA-PLP), Amplitude Modulation Spectrogram (AMS), and Multi-Resolution Cochleagram (MRCG), on PESQ and STOI under varying noise conditions. Experimental outcomes underscore that MRCG consistently emerges as the optimal and most stable feature for STOI, while the feature yielding the highest PESQ score exhibits intricate correlations with the background noise type, SNR level, and noise compatibility with the speech signal. Consequently, we propose an SE methodology founded on quality assessment and feature selection, facilitating the adaptive selection of optimal features tailored to distinct background noise scenarios, thereby always maintain the highest caliber enhancement effect with regard to PESQ metrics.

SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and Denoising AutoEncoder.

Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these methods still lack generalization across different datasets, hindering their widespread use. In this paper, an effective model named Swin Denoising AutoEncoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.

Enhancing dimensional accuracy in 3D printing: a novel software algorithm for real-time quality assessment

Enhancing dimensional accuracy in 3D printing: a novel software algorithm for real-time quality assessment