Video Quality Assessment Research Articles

Objective quality assessment of medical images and videos: review and challenges

Objective quality assessment of medical images and videos: review and challenges

Evaluating the quality and reliability of youtube videos on achilles tendinopathy: a comprehensive analysis

Aims: This study evaluates the quality, reliability, and educational value of YouTube videos on Achilles tendinopathy. Methods: A YouTube search using the keyword “Achilles tendinopathy” was conducted on June 20, 2024, using an incognito browser tab. The first 50 English-language videos were analyzed for upload date, duration, views, likes, dislikes, comments, and categorized by source and content. The DISCERN score, Global Quality Score (GQS), and Journal of the American Medical Association (JAMA) score were used to assess video quality and reliability. Statistical analyses included the Shapiro-Wilk test, Kruskal-Wallis test, Mann-Whitney U test, and Spearman test for correlations. Results: Among 50 videos, the average DISCERN score was 42.5, GQS was 3.2, and JAMA score was 2.6, indicating moderate overall quality. Academic physician videos had higher scores. Exercise training videos scored significantly higher in quality assessments. The highest Video Power Index (VPI) was also found in videos by academic physicians. Conclusion: YouTube videos on Achilles tendinopathy provide moderately sufficient information, with higher quality in videos produced by academic physicians and those focusing on exercise training. The study suggests a need for standardized, high-quality educational content on online platforms.

SR4KVQA: Video quality assessment database and metric for 4K super-resolution

The quality assessment for 4K super-resolution (SR) videos can be conducive to the optimization of video SR algorithms. To improve the subjective and objective consistency of the SR quality assessment, a 4K video database and a blind metric are proposed in this paper. In the database SR4KVQA, there are 30 4K pristine videos, from which 600 SR 4K distorted videos with mean opinion score (MOS) labels are generated by three classic interpolation methods, six SR algorithms based on the deep neural network (DNN), and two SR algorithms based on the generative adversarial network (GAN). The benchmark experiment of the proposed database indicates that video quality assessment (VQA) of the 4K SR videos is challenging for the existing metrics. Among those metrics, the Video-Swin-Transformer backbone demonstrates tremendous potential in the VQA task. Accordingly, a blind VQA metric based on the Video-Swin-Transformer backbone is established, where the normalized loss function and optimized spatio-temporal sampling strategy are applied. The experiment result manifests that the Pearson linear correlation coefficient (PLCC) and Spearman rank-order correlation coefficient (SROCC) of the proposed metric reach 0.8011 and 0.8275 respectively on the SR4KVQA database, which outperforms or competes with the state-of-the-art VQA metrics. The database and the code proposed in this paper are available in the GitHub repository, https://github.com/AlexReadyNico/SR4KVQA.

Luminance decomposition and reconstruction for high dynamic range Video Quality Assessment

High dynamic range (HDR) video represents a wider range of brightness, detail and colour than standard dynamic range (SDR) video. However, SDR-based VQA (Video Quality Assessment) models struggle to capture HDR distortions. In addition, some of the existing methods designed for HDR video focus on emphasising the distortion of local areas of the video frame, ignoring the distortion of the video frame as a whole. Therefore, we propose a no reference VQA model based on luminance decomposition and recombination that provides excellent performance for HDR videos, called HDR-DRVQA. Specifically, HDR-DRVQA utilises a luminance decomposition strategy to decompose video frames into different regions for explicit extraction of perceptual features in different regions of the high dynamic range. We then further propose a residual aggregation module for recombining multi-region features to extract static spatial distortion representations and dynamic motion perception (captured by feature differences). Taking advantage of the Transformer network in remote dependency modelling, this information is fed into the Transformer network for interactive learning of motion perception and adaptively constructs a stream of spatial distortion information from shallow to deep layers during temporal aggregation. We validate that our model significantly outperforms SDR VQA and existing HDR VQA methods on the publicly available HDR databases.

Quality assessment of heatstroke videos on TikTok.

The prevalence of heatstroke is rising due to global warming, making it a serious but preventable condition, highlighting the urgent need for effective dissemination of relevant health education to the general public. Advances in technology have made accessing health information more convenient and rapid. In recent years, short videos have become a primary medium for delivering health education, with TikTok gaining considerable popularity among the general public. However, the quality of heatstroke-related health education content on TikTok deserves closer scrutiny. This study aimed to evaluate the quality and content of heatstroke-related videos available on TikTok. The present study analyzed the top 100 heatstroke-related short videos on TikTok, focusing on their characteristics, quality, and the content they conveyed. The quality of these videos was assessed using the DISCERN instrument. In addition, the completeness of the videos was assessed by examining six key aspects: disease definition, clinical manifestations, risk factors, assessment, management, and outcomes. The study included a total of 90 videos. The results showed that news organizations and healthcare professionals were the primary contributors to these videos, with those from news organizations receiving the most attention. In contrast, those from healthcare professionals received comparatively less engagement. Overall, the quality of the information was found to be moderately low, with the highest quality videos posted by non-profit organizations, followed by those posted by healthcare professionals. The majority of videos uploaded described the disease definition, clinical presentation, risk factors, assessment, management, and outcomes of heatstroke. The quality of information provided in heatstroke-related short videos on TikTok is generally inadequate and requires significant improvement. In addition, such content should be subject to government review to ensure its accuracy and reliability.

Explainable machine learning for assessing upper respiratory tract of racehorses from endoscopy videos

Laryngeal hemiplegia (LH) is a major upper respiratory tract (URT) complication in racehorses. Endoscopy imaging of horse throat is a gold standard for URT assessment. However, current manual assessment faces several challenges, stemming from the poor quality of endoscopy videos and subjectivity of manual grading. To overcome such limitations, we propose an explainable machine learning (ML)-based solution for efficient URT assessment. Specifically, a cascaded YOLOv8 architecture is utilized to segment the key semantic regions and landmarks per frame. Several spatiotemporal features are then extracted from key landmarks points and fed to a decision tree (DT) model to classify LH as Grade 1,2,3 or 4 denoting absence of LH, mild, moderate, and severe LH, respectively. The proposed method, validated through 5-fold cross-validation on 107 videos, showed promising performance in classifying different LH grades with 100%, 91.18%, 94.74% and 100% sensitivity values for Grade 1 to 4, respectively. Further validation on an external dataset of 72 cases confirmed its generalization capability with 90%, 80.95%, 100%, and 100% sensitivity values for Grade 1 to 4, respectively. We introduced several explainability related assessment functions, including: (i) visualization of YOLOv8 output to detect landmark estimation errors which can affect the final classification, (ii) time-series visualization to assess video quality, and (iii) backtracking of the DT output to identify borderline cases. We incorporated domain knowledge (e.g., veterinarian diagnostic procedures) into the proposed ML framework. This provides an assistive tool with clinical-relevance and explainability that can ease and speed up the URT assessment by veterinarians.

Objective Quality Assessment of Stereoscopic Video Using Inflated 3D Features

Objective Quality Assessment of Stereoscopic Video Using Inflated 3D Features

HDR-ChipQA: No-reference quality assessment on High Dynamic Range videos

We present a no-reference video quality model and algorithm that delivers standout performance for High Dynamic Range (HDR) videos, which we call HDR-ChipQA. HDR videos represent wider ranges of luminances, details, and colors than Standard Dynamic Range (SDR) videos. The growing adoption of HDR in massively scaled video networks has driven the need for video quality assessment (VQA) algorithms that better account for distortions on HDR content. In particular, standard VQA models may fail to capture conspicuous distortions at the extreme ends of the dynamic range, because the features that drive them may be dominated by distortions that pervade the mid-ranges of the signal. We introduce a new approach whereby a local expansive nonlinearity emphasizes distortions occurring at the higher and lower ends of the local luma range, allowing for the definition of additional quality-aware features that are computed along a separate path. These features are not HDR-specific, and also improve VQA on SDR video contents, albeit to a reduced degree. We show that this preprocessing step significantly boosts the power of distortion-sensitive natural video statistics (NVS) features when used to predict the quality of HDR content. In similar manner, we separately compute novel wide-gamut color features using the same nonlinear processing steps. We have found that our model significantly outperforms SDR VQA algorithms on the only publicly available, comprehensive HDR database, while also attaining state-of-the-art performance on SDR content.

A Spatial-Temporal Video Quality Assessment Method via Comprehensive HVS Simulation.

The quality of videos is the primary concern of video service providers. Built upon deep neural networks, video quality assessment (VQA) has rapidly progressed. Although existing works have introduced the knowledge of the human visual system (HVS) into VQA, there are still some limitations that hinder the full exploitation of HVS, including incomplete modeling with few HVS characteristics and insufficient connection among these characteristics. In this article, we present a novel spatial-temporal VQA method termed HVS-5M, wherein we design five modules to simulate five characteristics of HVS and create a bioinspired connection among these modules in a cooperative manner. Specifically, on the side of the spatial domain, the visual saliency module first extracts a saliency map. Then, the content-dependency and the edge masking modules extract the content and edge features, respectively, which are both weighted by the saliency map to highlight those regions that human beings may be interested in. On the other side of the temporal domain, the motion perception module extracts the dynamic temporal features. Besides, the temporal hysteresis module simulates the memory mechanism of human beings and comprehensively evaluates the video quality according to the fusion features from the spatial and temporal domains. Extensive experiments show that our HVS-5M outperforms the state-of-the-art VQA methods. Ablation studies are further conducted to verify the effectiveness of each module toward the proposed method. The source code is available at https://github.com/GZHU-DVL/HVS-5M.

TikTok and pediatric nephrology: content quality assessment of videos related to pediatric kidney disease and kidney transplant.

Social media platforms such as TikTok™ are key sources of health information for young patients and caregivers. Misinformation is prevalent on TikTok™ across healthcare fields, which can perpetuate false beliefs about medical care. Limited data exists on the reliability of pediatric nephrology TikTok™ content. This study aimed to describe the quality of medical content of TikTok™ Videos (TTVs), related to pediatric kidney disease and transplant. TTVs were selected using specific search terms and categorized into pediatric kidney disease and kidney transplant, excluding duplicate and adult-related content. The top 100 TTVs in each category, based on views, were analyzed. TTV characteristics were stratified by account type (physician, non-physician healthcare professional (HCP), non-HCP) and video aim (personal story, education, entertainment). DISCERN scoring, a validated questionnaire evaluating health information reliability, was conducted by 4 independent raters. Inter-rater reliability was assessed using a 2-way random effects model, and differences between content creator types were evaluated using one-way ANOVA and post-Hoc Tukey test. TTVs had a total of 12.5 million likes and 113.1 million views. Over 70% of videos were created by non-HCPs (n = 147/200). DISCERN scoring revealed low reliability of medical information across content creator types. TTVs created by physicians and non-physician HCPs about kidney disease had significantly higher mean DISCERN scores compared to those created by non-HCPs (2.85, p < 0.001 and 2.48, p = 0.005, respectively). Educators within the pediatric nephrology community must keep in mind the lack of reliability of medical information available on TikTok™ and coordinate collective efforts to consider utilizing TikTok™ for patient education.

No-reference video quality assessment based on human visual perception

No-reference video quality assessment based on human visual perception

A multi-scale no-reference video quality assessment method based on transformer

A multi-scale no-reference video quality assessment method based on transformer

ADS-VQA: Adaptive sampling model for video quality assessment

No-reference video quality assessment (NR-VQA) for user-generated content (UGC) plays a crucial role in ensuring the quality of video services. Although some works have achieved impressive results, their performance-complexity trade-off is still sub-optimal. On the one hand, overly complex network structures and additional inputs require more computing resources. On the other hand, the simple sampling methods have tended to overlook the temporal characteristics of the videos, resulting in the degradation of local textures and potential distortion of the thematic content, consequently leading to the performance decline of the VQA technologies. Therefore, in this paper, we propose an enhanced NR-VQA model, known as the Adaptive Sampling Strategy for Video Quality Assessment (ADS-VQA). Temporally, we conduct non-uniform sampling on videos utilizing features from the lateral geniculate nucleus (LGN) to capture the temporal characteristics of videos. Spatially, a dual-branch structure is designed to supplement spatial features across different levels. The one branch samples patches at their raw resolution, effectively preserving the local texture detail. The other branch performs a downsampling process guided by saliency cues, attaining global semantic features with a diminished computational expense. Experimental results demonstrate that the proposed approach achieves high performance at a lower computational cost than most state-of-the-art VQA models on four popular VQA databases.

CoSTA: Co-training spatial–temporal attention for blind video quality assessment

Self-attention-based Transformer has achieved great success in many computer vision tasks. However, its application to blind video quality assessment (VQA) is far from comprehensive. Evaluating the quality of in-the-wild videos is challenging due to the unknown of pristine reference and shooting distortion. This paper presents a Co-trained Space–Time Attention network for the blind VQA problem, termed CoSTA. Specifically, we first build CoSTA by alternately concatenating the divided space–time attention. Then, to facilitate the training of CoSTA, we design a vectorized regression loss by encoding the mean opinion score (MOS) to the probability vector and embedding a special token as the learnable variable of MOS, leading to the better fitting of the human rating process. Finally, to solve the data-hungry problem within Transformer, we propose to co-train the spatial and temporal attention weights using both images and videos. Various experiments are conducted on the de-facto in-the-wild video datasets, including LIVE-Qualcomm, LIVE-VQC, KoNViD-1k, YouTube-UGC, LSVQ, LSVQ-1080p, and DVL2021. Experimental results demonstrate the superiority of the proposed CoSTA over the state-of-the-art. The source code is publicly available at https://github.com/GZHU-DVL/CoSTA.

Quality of experience assessment in virtual/augmented reality serious games for healthcare: A systematic literature review

Virtual reality (VR) gaming (VRG) captures, examines, analysis, and renders the game data virtually instead of the users’ devices over efficient networks. E.g., VR is crucial in visually guided surgery and manifold therapies. Video Quality Assessment (VQA) helps VRG software and devices execute high-quality inputs/outputs. This paper debates VRG processes, environments, VQA metrics, Quality of Experience (QoE), platforms, current methods, tools, use in medicine, existing operational gains/challenges, and highlights future directions.

Development of Learning Videos on Petroleum Materials Using Dora Toon Web Apps for Class XI High School Students

Many students have difficulty understanding chemistry concepts due to limited learning resources such as textbooks and traditional lectures. This research proposes making videos using Dora Toon, an audiovisual web platform specifically for petroleum materials. This project aims to assess video quality through evaluation by research subjects (material experts, media, chemistry educators, and 10 class XI high school students). With a research and development approach, the model used is 4D (define, design, development, disseminate) with a focus on the disseminate stage. The definition stage includes analysis of the front-end, students, concepts, tasks, and learning objectives. The design stages include selecting media format, collecting references, creating initial designs, and making instruments. The development stage includes video production, validation tests on research subjects. Sampling used purposive sampling technique, data collection used a validation questionnaire, and analyzed descriptively in percentages. At the disseminate stage, the product can be distributed to the wider community. However, this stage was not carried out due to time constraints. This results in HD resolution that is easy to use. It produces HD (1280x720p) resolution MP4 videos that are easy to use and contain petroleum ingredients. The evaluation results were very positive. The material expert gave a score of 95% (very good), the media expert gave a score of 95% (very good), the chemistry teacher gave a score of 86.06% (very good), and the students answered positively 100%. These findings indicate that animated videos can be an alternative learning tool for teaching petroleum chemistry in the classroom

A Database and Model for the Visual Quality Assessment of Super-Resolution Videos

A Database and Model for the Visual Quality Assessment of Super-Resolution Videos

Spatial–Temporal Analysis-Based Video Quality Assessment: A Two-Stream Convolutional Network Approach

In system processing, video inevitably suffers from distortion, which leads to quality degradation and affects the user experience. Therefore, it is of great importance to design an accurate and effective objective video quality assessment (VQA) method. In this paper, by considering the multi-dimensional characteristics for video and visual perceptual mechanism, a two-stream convolutional network for VQA is proposed based on spatial–temporal analysis, named TSCNN-VQA. Specifically, for feature extraction, TSCNN-VQA first extracts spatial and temporal features by two different convolutional neural network branches, respectively. After that, the spatial–temporal joint feature fusion is constructed to obtain the joint spatial–temporal features. Meanwhile, the TSCNN-VQA also integrates an attention module to guarantee that the process conforms to the mechanism that the visual system perceives video information. Finally, the overall quality is obtained by non-linear regression. The experimental results in both the LIVE and CSIQ VQA datasets show that the performance indicators obtained by TSCNN-VQA are higher than those of existing VQA methods, which demonstrates that TSCNN-VQA can accurately evaluate video quality and has better consistency with the human visual system.

Objective Video Quality Assessment Method for Object Recognition Tasks

In the field of video quality assessment for object recognition tasks, accurately predicting the impact of different quality factors on recognition algorithms remains a significant challenge. Our study introduces a novel evaluation framework designed to address this gap by focussing on machine vision rather than human perceptual quality metrics. We used advanced machine learning models and custom Video Quality Indicators to enhance the predictive accuracy of object recognition performance under various conditions. Our results indicate a model performance, achieving a mean square error (MSE) of 672.4 and a correlation coefficient of 0.77, which underscores the effectiveness of our approach in real-world scenarios. These findings highlight not only the robustness of our methodology but also its potential applicability in critical areas such as surveillance and telemedicine.

Encoding laparoscopic image to words using vision transformer for distortion classification and ranking in laparoscopic videos

AbstractLaparoscopic videos are tools used by surgeons to insert narrow tubes into the abdomen and keep the skin without large incisions. The videos captured by a camera are prone to numerous distortions such as uneven illumination, motion blur, defocus blur, smoke, and noise which have impact on visual quality. Automatic detection and identification of distortions are significant to enhance the quality of laparoscopic videos to avoid errors during surgery. The video quality assessment includes two stages: classification of distortions affecting the video frames to identify their types and ranking of distortions to estimate the intensity levels. The dataset generated in ICIP2020 challenge including laparoscopic videos was utilized for training, validation, and testing the proposed solution. The difficulty of this dataset is caused by having five categories of distortions and four levels of severity. Additionally, the availability of multiple distortion categories in one video is considered the most challenging part of this dataset. The work presented in this paper contributes to solve the multi-label distortion classification and ranking problem. This paper aims to enhance the performance of distortion classification solutions. Vision transformer which is a deep learning model was used to extract informative features by transferring learning and representation from the general domain to the medical domain (laparoscopic videos). Additionally, six parallel multilayer perceptron (MLP) classifiers were added and attached to vision transformer for distortion classification and ranking. The experiment showed that the proposed solution outperforms existing distortion classification methods in terms of average accuracy (89.7%), average single distortion F1 score (94.18%), and average of both single and multiple distortions F1 score (96.86%). Moreover, it can also rank the distortions with an average accuracy of 79.22% and average F1 score of 78.44%. Hence, the high performance of the method proposed in this paper opens the door to integrate our solution in the intelligent video enhancement system.