Real-time video quality analysis on mobile devices

Real-time screen-sharing provides users with ubiquitous access to remote applications, such as computer games, movie players, and desktop applications (apps), anywhere and anytime. In this article, we study the performance of different screen-sharing technologies, which can be classified into native and clientless ones. The native ones dictate that users install special-purpose software, while the clientless ones directly run in web browsers. In particular, we conduct extensive experiments in three steps. First, we identify a suite of the most representative native and clientless screen-sharing technologies. Second, we propose a systematic measurement methodology for comparing screen-sharing technologies under diverse and dynamic network conditions using different performance metrics. Last, we conduct extensive experiments and perform in-depth analysis to quantify the performance gap between clientless and native screen-sharing technologies. We found that our WebRTC-based implementation achieves the best overall performance. More precisely, it consumes a maximum of 3 Mbps bandwidth while reaching a high decoding ratio and delivering good video quality. Moreover, it leads to a steadily high decoding ratio and video quality under dynamic network conditions. By presenting the very first rigorous comparisons of the native and clientless screen-sharing technologies, this article will stimulate more exciting studies on the emerging clientless screen-sharing technologies.

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DT-CNN: Dilated and Transposed Convolution Neural Network Accelerator for Real-Time Image Segmentation on Mobile Devices

2019 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas.2019.8702243 ◽

2019 ◽

Cited By ~ 8

Author(s):

Dongseok Im ◽

Donghyeon Han ◽

Sungpill Choi ◽

Sanghoon Kang ◽

Hoi-Jun Yoo

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Real Time ◽

Mobile Devices ◽

Convolution Neural Network ◽

Real Time Image ◽

Time Image

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Real-Time Video Quality Control For Multimedia Network

2013 International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications ◽

10.1109/mobilware.2013.25 ◽

2013 ◽

Cited By ~ 1

Author(s):

Biao Jiang ◽

Tarek Saadawi

Keyword(s):

Quality Control ◽

Real Time ◽

Video Quality ◽

Multimedia Network

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A prototype of a real-time solution on mobile devices for heart tele-auscultation

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments - PETRA '15 ◽

10.1145/2769493.2769547 ◽

2015 ◽

Cited By ~ 1

Author(s):

Julio Cesar Bellido ◽

Giuseppe De Pietro ◽

Giovanna Sannino

Keyword(s):

Real Time ◽

Mobile Devices ◽

Time Solution

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Evaluation of quality and reliability of musculoskeletal ultrasound videos on YouTube

Modern Rheumatology ◽

10.1093/mr/roab062 ◽

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.

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