scholarly journals Weighted-MSE based on saliency map for assessing video quality of H.264 video streams

2011 ◽  
Author(s):  
H. Boujut ◽  
J. Benois-Pineau ◽  
O. Hadar ◽  
T. Ahmed ◽  
P. Bonnet
Keyword(s):  
Video Quality ◽  
Saliency Map ◽  
Video Streams

scholarly journals Distortion-Based Slice Level Prioritization for Real-Time Video over QoS-Enabled Wireless Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Ismail A. Ali ◽  
Martin Fleury ◽  
Mohammed Ghanbari
Keyword(s):  
Wireless Networks ◽  
Quality Of Service ◽  
Real Time ◽  
Wireless Lan ◽  
Video Quality ◽  
Ieee 802.11E ◽  
Video Streams ◽  
The Impact ◽  
Prioritization Scheme

This paper presents a prioritization scheme based on an analysis of the impact on objective video quality when dropping individual slices from coded video streams. It is shown that giving higher-priority classified packets preference in accessing the wireless media results in considerable quality gain (up to 3 dB in tests) over the case when no prioritization is applied. The proposed scheme is demonstrated for an IEEE 802.11e quality-of-service- (QoS-) enabled wireless LAN. Though more complex prioritization systems are possible, the proposed scheme is crafted for mobile interactive or user-to-user video services and is simply implemented within the Main or the Baseline profiles of an H.264 codec.

Inferring Streaming Video Quality from Encrypted Traffic

2021 ◽  
Vol 48 (3) ◽  
pp. 27-32
Author(s):  
Francesco Bronzino ◽  
Paul Schmitt ◽  
Sara Ayoubi ◽  
Guilherme Martins ◽  
Renata Teixeira ◽  
...  
Keyword(s):  
Service Providers ◽  
Video Quality ◽  
Composite Model ◽  
Streaming Video ◽  
Internet Service Providers ◽  
Video Streams ◽  
Internet Service ◽  
Startup Delay ◽  
Encrypted Traffic

Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so.We develop models that infer quality metrics (i.e., startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (e.g., due to aggregation). Second, we develop a single composite model that works for a range of different services (i.e., Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (e.g., the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed" and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.

Evaluation of the Quality, Educational Value and Utility of the Videos on YouTube for Laparoscopic Low Anterior Resection

2021 ◽  
pp. 000313482110111
Author(s):  
Erol Piskin ◽  
Muhammet Kadri Çolakoğlu ◽  
Ali Bal ◽  
Volkan Oter ◽  
Erdal Birol Bostanci
Keyword(s):  
Anterior Resection ◽  
Low Anterior Resection ◽  
Video Quality ◽  
Relevant Information ◽  
Teaching Quality ◽  
Educational Value ◽  
Laparoscopic Low Anterior Resection ◽  
Significant Difference ◽  
October 17

Background Minimally invasive surgery is a rising trend in colorectal surgery and is on its way to becoming the gold standard due to the benefits it provides for patients. This study aims to test the efficacy for educational purposes by evaluating the videos published on YouTube ( www.youtube.com ) channel for low anterior resection procedure in rectum surgery. Methods We searched YouTube on October 17, 2020 to choose video clips that included relevant information about laparoscopic low anterior resection (LAR) for rectal cancer. Results We included 25 academics and 75 individual videos in this study. The teaching quality of the videos was evaluated according to academic and individual videos, and it was seen that the teaching quality scores of academic videos were higher and this result was statistically significant ( P = .03). The modified Laparoscopic Surgery Video Educational Guidelines (LAP-VEGaS) criteria were found that the score was higher in individual videos ( P = .014). The median Video Power Index (VPI) value was 1.50 (range .05-347) and the mean ratio was 7.01 ± 3.52. There was no statistically significant difference between the 2 groups ( P = .443). Discussion Video-based surgical learning is an effective method for surgical education. Our study showed that the video quality and educational content of most of the videos about the low anterior resection procedure on YouTube were low. The videos of academic origin seem more valuable than individual videos. As far as video popularity is concerned, YouTube viewers are not selective. For this reason, training videos to be used for educational purposes must be passed through a standardized evaluation filter.

scholarly journals Adaptive Reservation of Network Resources According to Video Classification Scenes

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1949
Author(s):  
Lukas Sevcik ◽  
Miroslav Voznak
Keyword(s):  
User Satisfaction ◽  
Quality Evaluation ◽  
Spatial Information ◽  
Service Providers ◽  
Video Quality ◽  
Subjective Assessment ◽  
Video Sequences ◽  
Network Resources ◽  
Proposed Model

Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

scholarly journals Improving Perceived Quality of Live Adaptative Video Streaming

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 948
Author(s):  
Carlos Eduardo Maffini Santos ◽  
Carlos Alexandre Gouvea da Silva ◽  
Carlos Marcelo Pedroso
Keyword(s):  
Video Streaming ◽  
Short Term Memory ◽  
Transmission Rate ◽  
Video Quality ◽  
Video On Demand ◽  
Live Streaming ◽  
Network Congestion ◽  
Congested Networks ◽  
Over The Top

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

An Improved Error Concealment Based on Redundant Motion Vector Information

2012 ◽  
Vol 532-533 ◽  
pp. 1219-1224
Author(s):  
Hong Tao Deng
Keyword(s):  
Video Transmission ◽  
Error Concealment ◽  
Video Quality ◽  
Motion Vector ◽  
Compressed Video ◽  
Vector Information ◽  
Vector Estimation ◽  
Channel Errors ◽  
Bit Stream

During video transmission over error prone network, compressed video bit-stream is sensitive to channel errors that may degrade the decoded pictures severely. In order to solve this problem, error concealment technique is a useful post-processing tool for recovering the lost information. In these methods, how to estimate the lost motion vector correctly is important for the quality of decoded picture. In order to recover the lost motion vector, an Decoder Motion Vector Estimation (DMVE) criterion was proposed and have well effect for recover the lost blocks. In this paper, we propose an improved error concealment method based on DMVE, which exploits the accurate motion vector by using redundant motion vector information. The experimental results with an H.264 codec show that our method improves both subjective and objective decoder reconstructed video quality, especially for sequences of drastic motion.

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.

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