The Influence of Video Quality on Sponsorship Effects

2018 ◽  
Vol 23 (2) ◽  
pp. 97-114
Author(s):  
Sanghak Lee ◽  
Paul M Pedersen
Keyword(s):  
Video Quality

Objective Video Quality Method Based on Mutual Information and Human Visual System

2012 ◽  
Vol 58 (2) ◽  
pp. 147-152
Author(s):  
Michal Mardiak ◽  
Jaroslav Polec
Keyword(s):  
Mutual Information ◽  
Visual System ◽  
Human Visual System ◽  
Quality Evaluation ◽  
Video Quality ◽  
Evaluation Criteria ◽  
Test Sequence ◽  
Quality Metric ◽  
Two Stages ◽  
Video Quality Metric

Objective Video Quality Method Based on Mutual Information and Human Visual SystemIn this paper we present the objective video quality metric based on mutual information and Human Visual System. The calculation of proposed metric consists of two stages. In the first stage of quality evaluation whole original and test sequence are pre-processed by the Human Visual System. In the second stage we calculate mutual information which has been utilized as the quality evaluation criteria. The mutual information was calculated between the frame from original sequence and the corresponding frame from test sequence. For this testing purpose we choose Foreman video at CIF resolution. To prove reliability of our metric were compared it with some commonly used objective methods for measuring the video quality. The results show that presented objective video quality metric based on mutual information and Human Visual System provides relevant results in comparison with results of other objective methods so it is suitable candidate for measuring the video quality.

scholarly journals Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video

2021 ◽  
Vol 11 (11) ◽  
pp. 5270
Author(s):  
Waqas ur Rahman ◽  
Md Delowar Hossain ◽  
Eui-Nam Huh
Keyword(s):  
State Of The Art ◽  
Video Quality ◽  
Buffer Size ◽  
Bandwidth Utilization ◽  
Adaptation Algorithm ◽  
Control Laws ◽  
Quality Video ◽  
Video Player ◽  
High Quality Video ◽  
Abr Algorithm

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.

scholarly journals Impact of Scene Content on High Resolution Video Quality

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2872
Author(s):  
Miroslav Uhrina ◽  
Anna Holesova ◽  
Juraj Bienik ◽  
Lukas Sevcik
Keyword(s):  
Video Compression ◽  
Spatial Information ◽  
Video Quality ◽  
Video Sequences ◽  
Compression Efficiency ◽  
Opinion Score ◽  
Scene Description ◽  
Two Parameters ◽  
The University ◽  
The Impact

This paper deals with the impact of content on the perceived video quality evaluated using the subjective Absolute Category Rating (ACR) method. The assessment was conducted on eight types of video sequences with diverse content obtained from the SJTU dataset. The sequences were encoded at 5 different constant bitrates in two widely video compression standards H.264/AVC and H.265/HEVC at Full HD and Ultra HD resolutions, which means 160 annotated video sequences were created. The length of Group of Pictures (GOP) was set to half the framerate value, as is typical for video intended for transmission over a noisy communication channel. The evaluation was performed in two laboratories: one situated at the University of Zilina, and the second at the VSB—Technical University in Ostrava. The results acquired in both laboratories reached/showed a high correlation. Notwithstanding the fact that the sequences with low Spatial Information (SI) and Temporal Information (TI) values reached better Mean Opinion Score (MOS) score than the sequences with higher SI and TI values, these two parameters are not sufficient for scene description, and this domain should be the subject of further research. The evaluation results led us to the conclusion that it is unnecessary to use the H.265/HEVC codec for compression of Full HD sequences and the compression efficiency of the H.265 codec by the Ultra HD resolution reaches the compression efficiency of both codecs by the Full HD resolution. This paper also includes the recommendations for minimum bitrate thresholds at which the video sequences at both resolutions retain good and fair subjectively perceived quality.

scholarly journals Content Dependent Representation Selection Model for Systems Based on MPEG DASH

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1843
Author(s):  
Jelena Vlaović ◽  
Snježana Rimac-Drlje ◽  
Drago Žagar
Keyword(s):  
Mathematical Model ◽  
Spatial Information ◽  
Relevant Literature ◽  
Video Quality ◽  
Similarity Index ◽  
Video Encoding ◽  
Adaptation Algorithm ◽  
Subjective Testing ◽  
Proposed Model ◽  
The Mathematical Model

A standard called MPEG Dynamic Adaptive Streaming over HTTP (MPEG DASH) ensures the interoperability between different streaming services and the highest possible video quality in changing network conditions. The solutions described in the available literature that focus on video segmentation are mostly proprietary, use a high amount of computational power, lack the methodology, model notation, information needed for reproduction, or do not consider the spatial and temporal activity of video sequences. This paper presents a new model for selecting optimal parameters and number of representations for video encoding and segmentation, based on a measure of the spatial and temporal activity of the video content. The model was developed for the H.264 encoder, using Structural Similarity Index Measure (SSIM) objective metrics as well as Spatial Information (SI) and Temporal Information (TI) as measures of video spatial and temporal activity. The methodology that we used to develop the mathematical model is also presented in detail so that it can be applied to adapt the mathematical model to another type of an encoder or a set of encoding parameters. The efficiency of the segmentation made by the proposed model was tested using the Basic Adaptation algorithm (BAA) and Segment Aware Rate Adaptation (SARA) algorithm as well as two different network scenarios. In comparison to the segmentation available in the relevant literature, the segmentation based on the proposed model obtains better SSIM values in 92% of cases and subjective testing showed that it achieves better results in 83.3% of cases.

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.

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