Automatic Content Based Video Quality Analysis for Media Production and Delivery Processes

2009 ◽  
Author(s):  
Peter Schallauer ◽  
Hannes Fassold ◽  
Martin Winter ◽  
Werner Bailer ◽  
Georg Thallinger ◽  
...  
Keyword(s):  
Video Quality ◽  
Quality Analysis ◽  
Media Production

Evaluation of quality and reliability of musculoskeletal ultrasound videos on YouTube

2021 ◽  
Author(s):  
Nihan Cüzdan ◽  
İpek Türk
Keyword(s):  
Video Quality ◽  
Musculoskeletal Ultrasound ◽  
Quality Analysis ◽  
Competency Assessment ◽  
Exclusion Criteria ◽  
Educational Value ◽  
European League Against Rheumatism ◽  
Ultrasound Findings ◽  
Training Programmes

ABSTRACT Objectives To evaluate musculoskeletal ultrasound (MSUS) video contents on YouTube, regarding their quality, reliability, and educational value. Method The first three pages for the keywords ‘Musculoskeletal Ultrasound’, ‘joint ultrasound’, and ‘articular ultrasound’ were searched through YouTube website. The quality of the videos was assessed according to the European League Against Rheumatism (EULAR) Guidelines and EULAR Competency Assessment in MSUS. The reliability was evaluated with modified DISCERN tool. Results After the exclusion criteria applied, 58 videos were evaluated. The video quality analysis showed that probe holding (68.9%; median: 5, range: 0–5), scanning technique (63.8%; median: 4, range: 0–5), identification of anatomic structures (72.4%; median: 4, range: 0–5), and description of ultrasound findings (65.5%; median: 4, range: 0–5) were found to be sufficient, whereas ultrasound machine settings adjustments (1.7%; median: 0, range: 0–4) and final ultrasound diagnosis (12.1%; median: 0, range: 0–5) were insufficient. The total median value of the modified DISCERN scale was 2 (percentile: 2–2, range: 0–3). Conclusion MSUS video contents on YouTube are insufficient for educational purposes on MSUS training. There is a need for affordable, easily accessed, standardized, and peer-reviewed online training programmes on MSUS and MSUS-guided injections.

Perceptual Video Quality Analysis for HEVC in Packet Loss Environment

2014 ◽  
Author(s):  
Bhupender Kumar ◽  
Shekhar Madnani ◽  
Advait Mogre ◽  
Muneesh Sharma ◽  
Shailesh Kumar
Keyword(s):  
Packet Loss ◽  
Video Quality ◽  
Quality Analysis ◽  
Perceptual Video Quality

scholarly journals A Quality Analysis Of Low Back Pain Videos On Youtube

2020 ◽  
Author(s):  
Muhammet Arif Özbek ◽  
Oguz Baran ◽  
Şevket Evran ◽  
Ahmet Kayhan ◽  
Tahsin Saygı ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Medical Information ◽  
Average Length ◽  
Video Quality ◽  
Scoring Systems ◽  
Quality Analysis ◽  
Low Back ◽  
The Subject ◽  
Youtube Videos

Abstract Background: Most people face low back pain problems at least once in their lifetimes. With the advancing technology, people have been consulting the internet regarding their diagnoses more and more over the last 20 years. This study aims to evaluate the accuracy and reliability of YouTube videos on low back pain. Methods: The keyword “Low Back Pain” was used in our search on YouTube. The first 50 videos to come up in the search results were evaluated using JAMA, DISCERN, and GQS scoring systems. The individual correlation of each video and the correlation between the aforementioned scoring systems were statistically analyzed. Results: The average length of the 50 videos that were analyzed is 7,57 minutes (0,34 – 48,23 minutes), and the average daily view count of the videos is 331,14. Generally, video quality was found to be “poor”. On average, JAMA score was 1,64, DISCERN score was 1,63 and GQS score was 1,93. The most common videos found on the subject were those that were done by TV programs. And, videos by health information websites and by Hospitals / Doctors / Educational Institutions were, while still being below the threshold value, found to give higher quality information on the subject than the videos by other sources. Conclusion: Videos on YouTube regarding low back pain are of low quality, and most are created by unreliable sources. Therefore, such YouTube videos should not be recommended as patient education tools on low back pain. An important step in disseminating correct medical information to the public would be to have a platform where the accuracy and quality of given medical information are evaluated by medical experts.

Video Quality Analysis for Concert Video Mashup Generation

2010 ◽  
pp. 1-12 ◽  
Author(s):  
Prarthana Shrestha ◽  
Hans Weda ◽  
Mauro Barbieri ◽  
Peter H. N. de With
Keyword(s):  
Video Quality ◽  
Quality Analysis

Quality Analysis of YouTube Videos Presenting Shoulder Exercises after Breast Cancer Surgery

Breast Care ◽  
2021 ◽  
pp. 1-11
Author(s):  
Alvaro Manuel Rodriguez Rodriguez ◽  
María Blanco-Diaz ◽  
Pedro Lopez Diaz ◽  
Marta de la Fuente Costa ◽  
Lirios Dueñas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Video Quality ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Quality Analysis ◽  
Breast Cancer Surgery ◽  
Translational Activity ◽  
Youtube Videos ◽  
Prolonged Immobilization

<b><i>Background:</i></b> The prolonged immobilization suggested after breast cancer (BC) surgery causes morbidity. Patients search the Internet, especially social networks, for recommended exercises. <b><i>Objective:</i></b> The aim of this observational study was to assess the quality of YouTube videos, accessible for any patient, about exercises after BC surgery. <b><i>Methods:</i></b> A systematic search was performed on YouTube. One hundred and fifty videos were selected and analyzed. Two statistical analyses were conducted based on machine-learning techniques. Videos were classified as “Relevant” and “Non-Relevant” using principal component analysis models. Popularity was evaluated by Video Power Index (VPI), informational quality and accuracy were measured using the DISCERN Scale and Global Quality Scale (GQS). Scoring criteria for exercises were established according to the exercises recommended by the Oncology Section of the American Physical Therapy Association (APTA). Interobserver agreement and individual correlations were statistically examined. <b><i>Results:</i></b> DISCERN scored a mean of 50.97 (standard deviation [SD] 19.19). HONcode scored 78.30 (11.02) and GQS scored 3.49 (0.74). Average number of views was 53,963 (SD 67,376), mean duration was 9:42 min (9:15), mean days online was 2,158 (922), mean view ratio was 27.14 (30.24), mean likes was 245 (320.5), mean dislikes was 13.4 (14.2), and mean VPI was 93.48 (5.42). <b><i>Conclusion:</i></b> The quality of YouTube videos of recommended exercises post-BC surgery is high and can be a translational activity to improve patients’ behavior. Health institutions and NGOs, with higher popularity levels than academic institutions, should consider this information when implementing new policies focused on video quality which can contribute to adaptive behavior in patients.

Real-time video quality analysis on mobile devices

2015 ◽  
Author(s):  
Hannes Fassold ◽  
Stefanie Wechtitsch ◽  
Marcus Thaler ◽  
Krzysztof Kozłowski ◽  
Werner Bailer
Keyword(s):  
Real Time ◽  
Mobile Devices ◽  
Video Quality ◽  
Quality Analysis

scholarly journals Watermarking Video Menggunakan Metode Transformasi Wavelet Diskrit

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Syamsuryadi Syamsuryadi ◽  
Ibnu Aqil
Keyword(s):  
Color Image ◽  
Video Quality ◽  
Extraction Process ◽  
Video Watermarking ◽  
Quality Analysis ◽  
Discrete Wavelet ◽  
Original Video ◽  
Binary Images ◽  
Video Watermark ◽  
The Difference

<p align="center"><strong><em>Abstract</em></strong></p><p><em>Watermarking videos are useful to determine authentication rights to a video. The step of watermarking is by inserting binary images on an MPEG1 format video using discrete wavelet transformations, and extracting watermark videos. Analysis of video watermark quality can be known by calculatong PSNR. Video watermark quality analysis is done after carrying out the watermarking and extraction process to find out the difference the original video quality and the video watermark and the insertion image used. The results of video watermark quality analysis showed that 100% video watermarks did not change from the original video and binary imagery was better than the color image in the insert image.</em></p><p><strong><em>Keyword </em></strong><em>: video watermarking, authentication, binary imagery, insert image, discrete wavelet transformation, PSNR.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Watermarking video berguna untuk menentukan hak otentikasi terhadap suatu video. Tahapan watermarking adalah penyisipan citra biner terhadap suatu video berformat MPEG1 menggunakan transformasi wavelet diskrit, dan melakukan ekstraksi terhadap video watermark. Analisis kualitas video watermark dapat diketahui dengan perhitungan PSNR. Analisis kualitas video watermark dilakukan setelah melakukan proses watermarking dan ekstraksi untuk mengetahui apakah perbedaan  kualitas video asli dengan video watermark dan citra sisipan yang digunakan. Hasil analisis kualitas video watermark menunjukkan bahwa 100% video watermark tidak mengalami perubahan dari video asli dan citra biner lebih baik daripada citra berwarna pada citra sisipan.</em></p><p><strong><em>Kata kunci </em></strong><em>: watermarkingi video, otentikasi, citra biner, citra sisipan, transformasi wavelet diskrit, PSNR. </em></p>

Open Software Framework for Collaborative Development of No Reference Image and Video Quality Metrics

2020 ◽  
Vol 2020 (11) ◽  
pp. 92-1-92-6
Author(s):  
Margaret H. Pinson ◽  
Philip J. Corriveau ◽  
Mikołaj Leszczuk ◽  
Michael Colligan
Keyword(s):  
Video Quality ◽  
Quality Analysis ◽  
Reference Image ◽  
Software Framework ◽  
Collaborative Development ◽  
Image Quality Analysis ◽  
Face To Face ◽  
Ongoing Work ◽  
Support Tools

This paper describes ongoing work within the video quality experts group (VQEG) to develop no-reference (NR) audiovisual video quality analysis (VQA) metrics. VQEG provides an open forum that encourages knowledge sharing and collaboration. The VQEG no-reference Metric (NORM) group’s goal is to develop open-source NR-VQA metrics that meet industry requirements for scope, accuracy, and capability. This paper presents industry specifications from discussions at VQEG face-to-face meetings among industry, academic, and government participants. This paper also announces an open software framework for collaborative development of NR image quality Analysis (IQA) and VQA metrics <ext-link ext-link-type="url" xlink:href="https://github.com/NTIA/NRMetricFramework"><https://github.com/NTIA/NRMetricFramework></ext-link>. This framework includes the support tools necessary to begin research and avoid common mistakes. VQEG’s goal is to produce a series of NR-VQA metrics with progressively improving scope and accuracy. This work draws upon and enables IQA metric research, as both use the human visual system to analyze the quality of audiovisual media on modern displays. Readers are invited to participate.

Video quality analysis framework for spatial and temporal artifacts

2018 ◽  
Author(s):  
Yilin Wang ◽  
Balu Adsumilli
Keyword(s):  
Video Quality ◽  
Quality Analysis ◽  
Analysis Framework
Sign in / Sign up

Export Citation Format

Share Document