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This paper proposed a distributed video streaming fair scheduling strategy based on federated deep reinforcement learning to address the issues of unfair user QoE and low bandwidth utilization caused by uneven video bandwidth allocation in bottleneck links. This strategy dynamically generated bandwidth allocation weights based on the client's network status and the QoE level of each video stream. The congestion control algorithm at the server side allocated bandwidth to each video stream in the bottleneck link according to the computed weights, ensuring equitable transmission of video streams in the bottleneck link. Each video terminal operated a bandwidth allocation agent, and multiple agents train periodically using federated learning to facilitate rapid convergence of the agent models. The bandwidth allocation agents synchronized their training parameters through a consensus mechanism, enabling distributed aggregation of the agent model parameters while ensuring the security of parameter sharing. Experimental results demonstrate that the proposed strategy improves QoE fairness and overall QoE efficiency by 10% and 7%, respectively, compared to the latest solutions. This indicates that the proposed strategy has potential and effectiveness in addressing the uneven allocation of video stream bandwidth and improving user experience. [ABSTRACT FROM AUTHOR]

We propose a deep learning-based cross-layer power allocation method for asymmetric cell-free massive MIMO video communication systems. The proposed cross-layer approach considers physical layer channel state information (CSI) and the application layer rate distortion (RD) function, and it aims to enhance video quality in terms of peak signal-to-noise ratio (PSNR). Our study develops a decentralized deep neural network (DNN) model to capture intricate system patterns, enabling accurate and efficient power allocation decisions. The proposed cross-layer approach includes unsupervised and hybrid (supervised/unsupervised) learning models. The numerical results show that the hybrid method achieves convergence with just 50% of the iterations required by the unsupervised learning model and that it achieves a 1 dB gain in PSNR over the baseline physical layer scheme. [ABSTRACT FROM AUTHOR]

Video data received for analysis often come in a variety of file formats and compression schemes. These data are often transcoded to a consistent file format for forensic examination and/or ingesting into a video analytic system. The file format often requested is an MP4 file format. The MP4 file format is a very common and a universally accepted file format. The practical application of this transcoding process, across the analytical community, has generated differences in video quality. This study sought to explore possible origins of the differences and assist the practitioner by defining minimum recommendations to ensure that quality of the video data is maintained through the transcoding process. This study sought to generate real world data by asking participants to transcode provided video files to an MP4 file format using programs they would typically utilize to perform this task. The transcoded results were evaluated based on measurable metrics of quality. As the results were analyzed, determining why these differences might have occurred became less about a particular software application and more about the settings employed by the practitioner or of the capabilities of the program. This study supports the need for any video examiner who is transcoding video data to be cognizant of the settings utilized by the programs employed for transcoding video data, as loss of video quality can affect analytics as well as further analysis. [ABSTRACT FROM AUTHOR]

Changes in the footballing world's approach to technology and innovation, along with major advancements in broadcasting contributed to the decision by the International Football Association Board to introduce Video Assistant Referees in 2018. The change meant that under strict protocols referees could use video replays to review decisions in the event of a "clear and obvious error" or a "serious missed incident". At the time of writing 48-Member Associations have introduced the Video Assistant Referees protocol in at least one of their tournaments and there are many technology providers who work with organisers to implement the Video Assistant Referees systems. To ensure that the use of Video Assistant Referees has a positive effect on the game, Fédération Internationale de Football Association collaborated with the RISE Research Institutes of Sweden to develop objective test methods that could be used to ensure that a system can provide an adequate solution. Each provider must be able to pass requirements that ensure that they can deal with the challenges of processing, coding, decoding, synchronising, and re-formatting of the broadcast feeds. This article will describe the development of the test methods and illustrate some initial results from a test event on Video Assistant Referees system candidates. The methods have shown to be robust and appropriate for their intended purpose and will be developed over the years to ensure the quality of Video Assistant Referees. The developed measurement methods are general and can be applied to other broadcast and video systems as well as to other sports. [ABSTRACT FROM AUTHOR]

Video delivered over IP networks in real-time applications, which utilize RTP protocol over unreliable UDP such as videotelephony or live-streaming, is often prone to degradation caused by multiple sources. The most significant is the combined effect of video compression and its transmission over the communication channel. This paper analyzes the adverse impact of packet loss on video quality encoded with various combinations of compression parameters and resolutions. For the purposes of the research, a dataset containing 11,200 full HD and ultra HD video sequences encoded to H.264 and H.265 formats at five bit rates was compiled with a simulated packet loss rate (PLR) ranging from 0 to 1%. Objective assessment was conducted by using peak signal to noise ratio (PSNR) and Structural Similarity Index (SSIM) metrics, whereas the well-known absolute category rating (ACR) was used for subjective evaluation. Analysis of the results confirmed the presumption that video quality decreases along with the rise of packet loss rate, regardless of compression parameters. The experiments further led to a finding that the quality of sequences affected by PLR declines with increasing bit rate. Additionally, the paper includes recommendations of compression parameters for use under various network conditions. [ABSTRACT FROM AUTHOR]

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client–server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video. [ABSTRACT FROM AUTHOR]

With the widespread usage of video capture devices and social media videos, videos are dominating the multimedia landscape. There is an emerging need for video quality assessment (VQA) that forms the backbone of advanced video systems. Night-time videos play an important role in user capturing, hence being able to accurately assess their quality is critical. However, the characteristics of night-time videos differ from those of general in-capture videos; and VQA algorithms that have been developed for general-purpose videos cannot accurately assess the quality of night-time videos. Research is needed to gain a better understanding of how humans perceive the quality of night-time videos, and use this new understanding to develop reliable VQA algorithms. To this end, we construct a large-scale night-time VQA database, namely Mobile In-capture Night-time Database for Video Quality (MIND-VQ), containing 1181 night-time videos, 435 subjects, and over 130000 opinion scores. We perform thorough analyses to reveal subjective quality assessment behaviors of night-time videos. Furthermore, we propose a new VQA model, namely Visibility-based Night-time Video Quality Assessment Network, VINIA. Spatial and temporal visibility-aware components are characterized to reflect properties of human perception of night-time VQA task. A series of experiments are conducted to compare our VINIA with other existing VQA algorithms using our new MIND-VQ database and other public VQA databases. Experimental results show that our subjective VQA database provides new insights and our new VINIA model achieves superior performance in accessing night-time video quality. [ABSTRACT FROM AUTHOR]

With increasing numbers of laparoscopic hysterectomies, surgical trainees are compelled to learn more about endoscopy. Owing to coronavirus disease-related social distancing requirements, online education has gained prominence. Here, we aimed to investigate the laparoscopic hysterectomy video quality on YouTube using the LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS). YouTube was searched on June 7, 2020 using 'laparoscopic hysterectomy'. Three examiners evaluated videos using Global Operative Assessment of Laparoscopic Skills (GOALS). Subsequently, videos were assessed for their conformity to the LAP-VEGaS and LAP-VEGaS Video Assessment Tool. Interobserver reliability was estimated using intraclass coefficients and Cronbach's alpha. Cochran's Q test was used to determine correlations among quantitative data. The median GOALS score was 21.50. The observers' GOALS scores were significantly correlated. The results showed low conformity to the LAP-VEGaS. YouTube is the most used platform among trainees. The low YouTube video educational quality highlights the necessity for peer review, as trainees increasingly seek such resources during the pandemic. What is already known on this subject? YouTube is the most commonly used online resource for educational material among surgical trainees. Online videos usually do not undergo a peer-review process. The LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS) may be used to assess the educational quality of surgical videos. What do the results of this study add? To our knowledge, this is the first study on the quality of laparoscopic hysterectomy videos available on YouTube and the first study to evaluate YouTube laparoscopic surgery videos using the LAP-VEGaS Video Assessment Tool (VAT). Our study revealed the low educational quality of YouTube laparoscopic hysterectomy videos. The LAP-VEGaS VAT seems to be a valid and practical tool for assessing online laparoscopic hysterectomy videos. What are the implications of these findings for clinical practice and/or further research? Medical communities, especially tertiary care or academic centres, may upload educational peer-reviewed videos for trainees seeking this type of resource, especially during the coronavirus disease pandemic, as surgical education alternatives are limited. [ABSTRACT FROM AUTHOR]

Medical education instructional videos are more popular and easier to create than ever before. Standard quality measures for this medium do not exist, leaving educators, learners, and content creators unable to assess these videos. Drawing from the literature on video quality and popularity, reusable learning objects, and multimedia and curriculum development principles, we developed a 26-item instructional video quality checklist (IVQC), to capture aspects of educational design (six items), source reliability (four items), multimedia principle adherence (10 items), and accessibility (six items). Two raters applied IVQC to 206 videos from five producers across topics from two organ systems (cardiology and pulmonology) encompassing four disciplines (anatomy, physiology, pathology, and pharmacology). Inter-rater reliability was strong. According to two-rater means, eight multimedia items were present in over 80% of videos. A minority of videos included learning objectives (46%), alternative language translations (41%), when the video was updated (40%), analogies (37%), or references (9%). Producer ratings varied significantly (p <.001) across 17 of 26 items. There were no significant differences according to the video topic. IVQC detected differences in elements of instructional video quality. Future work can apply this instrument to a broader array of videos and in authentic educational settings. [ABSTRACT FROM AUTHOR]

Introduction: Carpal tunnel syndrome is a disease that reduces the quality of life, and it is characterized by numbness, tingling in the fingers, and weakness in the hand, which we frequently encounter in our daily clinical practice. In this study, we aimed to evaluate the videos in terms of quality and reliability by watching the first 50 YouTube videos most relevant to the carpal tunnel in the YouTube video channel, which is often the first reference point as a source of information. Method: Among the videos that appear after typing "carpal tunnel" in the search tab of the YouTube, we eliminated those with advertisements and those that are not in English and evaluated the top 50 most relevant videos about the carpal tunnel. A specific YouTube channel was not selected during the search; however, the most relevant videos on carpal tunnel syndrome were determined. The duration, the number of views, the number of likes, dislike numbers, upload times, and upload sources of the videos were recorded and evaluated. The content, quality, and reliability of the videos were evaluated according to the GQS, JAMA, and DISCERN scales. Results: The average length of the 50 videos included in the study was 315.18, the average number of views was 150,977.4, and the average number of likes was 1410.86. The average number of days when the videos were uploaded to the internet was calculated as 1259.62 days. The GQS average of 50 videos included in the study was calculated as 2.7, the JAMA score average as 2.14 and the DISCERN score average as 33.62, and the video quality, content, and reliability were low. There was no statistically significant relationship between uploading sources of videos to the internet and video content, quality, and reliability (p > 0.05). The number of views, the number of days uploaded to the internet, the number of views, the number of likes and dislikes, like rates, and the video power index of the videos showed no statistically significant relationship with JAMA, DISCERN, and GQS. Conclusion: Social media is one of the easiest methods to access information today. The high number of contents, quality, and reliability of social media videos are crucial for patients to obtain accurate information, gain awareness about diseases, and receive guidance on treatment. This study found out that the quality, content, and reliability of the existing videos on the carpal tunnel were at a low level. We propose that the videos' content and quality should be improved and become more beneficial for patients. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the user fairness power allocation (PA) for video transmission in a non-orthogonal multiple access (NOMA)-assisted wireless system with a pair of users. Considering that video transmission is usually delay-sensitive, the video quality can be adaptively adjusted based on the network conditions. First, a new performance metric, called the average video quality degradation probability, is proposed to describe the adaptive video quality. Then, based on the principles of NOMA and user queue model, the average video quality degradation probability of both users is derived. Next, a bisection search based power allocation (PA) algorithm is proposed to balance the video quality between the NOMA pair. Simulation results demonstrate that the proposed NOMA scheme is more effective to achieve the user fairness compared with the fixed NOMA scheme, the fractional transmit power allocation (FTPA) based NOMA scheme, the achievable rate fairness (ARF) NOMA scheme, and the optimized time division multiple access (TDMA) scheme. [ABSTRACT FROM AUTHOR]

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing Internet traffic on the video quality in an educational context is also presented. [ABSTRACT FROM AUTHOR]

Nowadays, video content accounts for a large percentage of network traffic. Most streaming services use HTTP Adaptive Streaming by splitting the video in non-overlapping segments and by encoding each video segment independently (possibly with multiple representations to allow adaptation to the varying network conditions). In this work we propose an encoding scheme based on a Deep Neural Network (DNN), to perform a per-title and per-segment adaptive estimation of the encoding parameter to achieve a target video quality of the encoded video. A dataset has been prepared using 1212 segments obtained from 158 videos. The segments have been encoded with 19 Constant Rate Factor (CRF) values using the VP9 encoder, generating a total of 23028 encoded segments, and for each encoded video segment, its Video Multi-Method Assessment Fusion (VMAF) quality has been computed. Besides, from a 240p downscaled version of the segments a feature vector has been obtained, and an analysis of the dependency of the features with the resolution has been carried out. With this dataset a DNN has been trained to estimate the CRF to be applied to each segment to achieve a target VMAF quality. Results show that the trained network is able to provide the CRF value to be applied to each video segment to achieve the desired quality with low computational overhead. To validate the proposal, the network has been used to predict the CRF to encode 1840 two-second segments, not used during the training process, using the VP9 codec at Full High Definition (FHD) resolution, and four target quality values. Results show that the system adapts the CRF to each segment and that the final videos have a mean deviation of 1.84% with respect to the requested VMAF value. [Display omitted] • Per-title and Per-segment estimation of CRF to achieve a target VMAF quality. • Generated dataset of 23028 encoded video segments. • Analysis of the dependency of the features with respect the resolution. • Mean VMAF target deviation of 1.84% for 11 tested videos. [ABSTRACT FROM AUTHOR]

We present a straightforward, non-intrusive adaptive bit rate streaming segment quality selection policy which aims at extending battery lifetime during playback while limiting the impact on the user’s quality of experience, thus benefiting consumers of video streaming services. This policy relies on the relationship between the available channel bandwidth and the bit rate of the representations in the quality ladder. It results from the characterization of the energy consumed by smartphones when running adaptive streaming client applications for different network connections (Wifi, 4G, and 5G) and the modeling of the energy consumed as a function of said relationship. Results show that a significant amount of energy can be saved (10 to 30%) by slightly modifying the default policy at the expense of a controlled reduction of video quality. [ABSTRACT FROM AUTHOR]

High‐efficiency video coding (HEVC) is the latest video coding standard aimed to reduce the bitrate by half for the same video quality compared to H.264/AVC. This encoding performance makes HEVC more suitable for high‐definition video applications. However, this performance is coupled with a high‐computational complexity, which makes it hard to achieve real‐time video encoding with a classic embedded processor. Multicore technology of programmable processors could be a very promising solution to overcome this computational complexity. Moreover, software optimisations by proposing fast algorithms for the most complex functions could also be an efficient solution to speed up the encoding process. In this context, this study presents a fast mode decision algorithm for the intra prediction module. This algorithm aims to reduce the number of intra prediction modes to be tested instead of performing a full intra mode search. Experimental results for all‐Intra configuration show that the proposed fast intra mode decision allows saving up to 46.79% of the intra prediction time in average. Encoding performance in terms of video quality and bitrate is not significantly affected. [ABSTRACT FROM AUTHOR]

The topics of visual and audio quality assessment (QA) have been widely researched for decades, yet nearly all of this prior work has focused only on single-mode visual or audio signals. However, visual signals rarely are presented without accompanying audio, including heavy-bandwidth video streaming applications. Moreover, the distortions that may separately (or conjointly) afflict the visual and audio signals collectively shape user-perceived quality of experience (QoE). This motivated us to conduct a subjective study of audio and video (A/V) quality, which we then used to compare and develop A/V quality measurement models and algorithms. The new LIVE-SJTU Audio and Video Quality Assessment (A/V-QA) Database includes 336 A/V sequences that were generated from 14 original source contents by applying 24 different A/V distortion combinations on them. We then conducted a subjective A/V quality perception study on the database towards attaining a better understanding of how humans perceive the overall combined quality of A/V signals. We also designed four different families of objective A/V quality prediction models, using a multimodal fusion strategy. The different types of A/V quality models differ in both the unimodal audio and video quality prediction models comprising the direct signal measurements and in the way that the two perceptual signal modes are combined. The objective models are built using both existing state-of-the-art audio and video quality prediction models and some new prediction models, as well as quality-predictive features delivered by a deep neural network. The methods of fusing audio and video quality predictions that are considered include simple product combinations as well as learned mappings. Using the new subjective A/V database as a tool, we validated and tested all of the objective A/V quality prediction models. We will make the database publicly available to facilitate further research. [ABSTRACT FROM AUTHOR]

With explosive increase of internet video services, perceptual modeling for video quality has attracted more attentions to provide high quality-of-experience (QoE) for end-users subject to bandwidth constraints, especially for compressed video quality. In this paper, a novel perceptual model for satisfied-user-ratio (SUR) on compressed video quality is proposed by exploiting compressed video bitrate changes and spatial-temporal statistical characteristics extracted from both uncompressed original video and reference video. In the proposed method, an efficient video feature set is explored and established to model SUR curves against bitrate variations by leveraging the Gaussian Processes Regression (GPR) framework. In particular, the proposed model is based on the recently released large-scale video quality dataset, VideoSet, and takes both spatial and temporal masking effects into consideration. To make it more practical, we further optimize the proposed method from three aspects including feature source simplification, computation complexity reduction and video codec adaption. Based on experimental results on VideoSet, the proposed method can accurately model SUR curves for various video contents and predict their required bitrates at given SUR values. Subjective experiments are conducted to further verify the generalization ability of the proposed SUR model. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the analysis of the existing approaches to video codecs comparisons. It includes the revealed drawbacks of popular comparison methods and proposes new techniques. The performed analysis of user-generated videos collection showed that two of the most popular open video collections from media.xiph.org which are widely used for video-codecs analysis and development do not cover real-life videos complexity distribution. A method for creating representative video sets covering all segments of user videos the spatial and temporal complexity is also proposed. One of the sections discusses video quality estimation algorithms used for video codec comparisons and shows the disadvantages of popular methods VMAF and NIQE. Also, the paper describes the drawbacks of the BD-rate – generally used method for video codecs final ranking during comparisons. A new ranking method called BSQ-rate which considers the identified issues is proposed. The results of this investigation were obtained during the series of research conducted as part of the annual video-codecs comparisons organized by video group of computer graphics and multimedia laboratory at Moscow State University. [ABSTRACT FROM AUTHOR]

A vehicle detection method fusing spatial saliency and local image features is presented in this paper to analyze the detection performance in compressed domain. Most of the existing visual saliency models use the input images, in which salient objects are to be detected and are free from complex background and overlapping areas. Moreover, they are very sensitive to the complex scene and different illuminations. In this method, the Scale Invariant Feature Transform and Harris features in combination with spatial saliency model play an important role in detecting vehicles from the scene. A one‐to‐one symmetric search is performed on the descriptors to select a set of matched interest point pairs for vehicle detection. We use 4K video of a road scene with different types of vehicles. The proposed method is able to detect desired overlapping objects from the road scene without heavy computation like other training‐based methods. In addition, the detection performance is analyzed in a H.265 compressed domain to help construct a relationship between objective and subjective evaluation value. The performance of this method is demonstrated by the experimental results. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper describes the process carried out to validate the application of one of the most robust and influential video quality metrics, Video Multimethod Assessment Fusion (VMAF), to 360VR contents. VMAF is a full reference metric initially designed to work with traditional 2D contents. Hence, at first, it cannot be assumed to be compatible with the particularities of the scenario where omnidirectional content is visualized using commercial head-mounted displays (HMDs). In this article, we prove that this metric can be successfully used to measure the quality of 360VR sequences without any specific training or adjustments, which evidences its usefulness and flexibility, and entails significant time and resource savings. Thus, it can be straightforwardly included in consumer appliances, namely content generators, servers and clients, as part of the embedded software or hardware as a reliable means to monitor the quality of the 360VR content consumed by users. [ABSTRACT FROM AUTHOR]

• PACDHE improving video quality. • PSNR, AMBE, Entropy metric. • Medical image processing. Recent years, videos are playing an important role in different applications such as pattern recognition, security purpose, news analysis, weather digest, and video browser and so on. Due to the importance of video applications, the quality of the video must be improved for making effective results in video analysis process. This paper introduces the particle optimization with adaptive cumulative distribution based histogram enhancement technique (PACDHE) for improving the video quality. Initially the videos are collected, each incoming frame has been analyzed and noise present in the video frame is eliminated by applying the non-divisional median filter. After that, quality of video is enhanced iteratively by examining each pixel present in video frames using the optimized fitness and cumulative distribution function. This process is repeated continuously until to enhance the video frames contrast and quality. Then the performance of the system is analyzed by using CV online video database and the efficiency is examined in terms of peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Entropy. The experimental results of PSO are compared with genetic algorithm based approach and found that PSO outperforms the GA approach and the existing histogram equalization approach and the existing histogram equalization approaches. [ABSTRACT FROM AUTHOR]

Although the high‐efficiency video codec plays a leading role among the video coding standards, it consumes a lot of memory resources and time processing due to its superior video quality and coding performance. The computation algorithm of rate distortion optimisation is one of the reasons behind the encoder's bottleneck. Therefore, a new technique has been introduced here based on the entropy threshold and size of coding unit, which reduces the coding time and bit rate to be more suitable for real‐time processing. According to the obtained results, the encoding time has been enhanced by 56.596% with a reduction in the bit rate reached to 74% on average. [ABSTRACT FROM AUTHOR]

This paper analyzes how an experimenter can balance errors in subjective video quality tests between the statistical power of finding an effect if it is there and not claiming that an effect is there if the effect is not there, i.e., balancing Type I and Type II errors. The risk of committing Type I errors increases with the number of comparisons that are performed in statistical tests. We will show that when controlling for this and at the same time keeping the power of the experiment at a reasonably high level, it is unlikely that the number of test subjects that are normally used and recommended by the International Telecommunication Union (ITU), i.e., 15 is sufficient but the number used by the Video Quality Experts Group (VQEG), i.e., 24 is more likely to be sufficient. Examples will also be given for the influence of Type I error on the statistical significance of comparing objective metrics by correlation. We also present a comparison between parametric and nonparametric statistics. The comparison targets the question whether we would reach different conclusions on the statistical difference between the video quality ratings of different video clips in a subjective test, based on the comparison between the student T-test and the Mann -- Whitney U-test. We found that there was hardly a difference when few comparisons are compensated for, i.e., then almost the same conclusions are reached. When the number of comparisons is increased, then larger and larger differences between the two methods are revealed. In these cases, the parametric T-test gives clearly more significant cases, than the nonparametric test, which makes it more important to investigate whether the assumptions are met for performing a certain test. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.5.053013] [ABSTRACT FROM AUTHOR]

In order to satisfy the experimental demand of scalable video transmission in the wireless mobile networl^， a simulation platform of scalable coding video in wireless mobile network is proposed. The simulation platform includes three phases. In the coding phase， source video is encoding as H. 264/SVC format. In the simulation phase，video packages are transmitted through mobile ad-hoc network，a new routing protocol is designed to get the available bandwidth estimation of the network，and this information is delivered to the adaptive bit rate adjust module. In the decoding phase，part of the " network abstraction layer units" are filtered， and the optimal decoded video streaming is abstracted. Besides，in order to transmit the information of adaptive adjustment of the scalable video streaming， a new routing protocol of mobile ad-hoc network is designed，the transmission bit rate of source node is adjusted according to the routing state and available resource of the network by adaptively feedback mechanism，and the demand of scalable video transmission is satisfied. The simulation experimental results show that the proposed platform can access some performance indices of scalable video streaming transmission through the wireless mobile network，and the proposed adaptive routing protocol can improve the video quality of the wireless mobile network. [ABSTRACT FROM AUTHOR]

In multimedia transmission, it is important to rely on an objective quality metric which accurately represents the subjective quality of processed images and video sequences. Maintaining acceptable Quality of Experience in video transmission requires the ability to measure the quality of the video seen at the receiver end. Reduced-reference metrics make use of side-information that is transmitted to the receiver for estimating the quality of the received sequence with low complexity. This attribute enables real-time assessment and visual degradation detection caused by transmission and compression errors. A novel reduced-reference video quality known as the Spatial Information in Salient Regions Reduced Reference Metric is proposed. The approach proposed makes use of spatial activity to estimate the received sequence distortion after concealment. The statistical elements analysed in this work are based on extracted edges and their luminance distributions. Results highlight that the proposed edge dissimilarit y measure has a good correlation with DMOS scores from the LIVE Video Database. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid development of mobile video services. The diversity of mobile devices and video services can result in a difference in a user's perceptual video quality, which presents a significant challenge for video quality evaluation. An objective assessment model is proposed that considers both video and screen characteristics, where the video characteristics include the video resolution and video coding quality (VCQ), and the screen characteristics include the screen size and screen resolution. By analyzing the relationships between the perceived video quality (PVQ) and the video and screen characteristics, an influential parameter, effective video pixels per inch (EV-PPI), is proposed that combines the two characteristics. Then, the PVQ is estimated with the VCQ and EV-PPI. Experiment results confirm that the proposed method can effectively estimate the user's PVQ. It can be used as a guideline for both video service providers and mobile device manufacturers to improve the performance of their services and devices. © 2018 SPIE and IS&T[DOI: 10.1117/1.JEI.27.3.033015] [ABSTRACT FROM AUTHOR]

A new methodology to measure coded image/video quality using the just-noticeable-difference (JND) idea was proposed in Lin et al. (2015). Several small JND-based image/video quality datasets were released by the Media Communications Lab at the University of Southern California in Jin et al. (2016) and Wang et al. (2016) [3]. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-s sequences in four resolutions (i.e., 1920 × 1080 , 1280 × 720 , 960 × 540 and 640 × 360 ). For each of the 880 video clips, we encode it using the H.264/AVC codec with QP = 1 , … , 51 and measure the first three JND points with 30 + subjects. The dataset is called the “VideoSet”, which is an acronym for “Video Subject Evaluation Test (SET)”. This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source/coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort (Wang et al., 2016 [4]). [ABSTRACT FROM AUTHOR]

Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models. © 2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.5.053012] [ABSTRACT FROM AUTHOR]

Multidimensional video scalability refers to the possibility that a video sequence can be adapted according to given conditions of video consumption by adjusting one or more of its features such as frame size, frame rate, and spatial quality. An important issue in implementing an adaptive video distribution scheme using scalability is how to maximize the quality of experience for the delivered contents, which raises a more fundamental issue, that is, how to estimate perceived quality of scalable video contents. This paper evaluates existing state-of-the-art objective quality metrics, including both generic image/video metrics and ones particularly developed for scalable videos, on the problem of quality assessment of multidimensional video scalability. It is shown that, on the whole, some recently developed metrics targeting scalability perform best. The results are thoroughly discussed in relation to the nature of the problem in comparison to what has been reported in existing studies for other problems. [ABSTRACT FROM AUTHOR]

The staggering mobile growth is shaping to be the biggest shift in technology since the advent of the Internet, paving the way for unprecedented and yet to be thought of video services and applications. The proliferation of these mobile devices and applications, however, requires new paradigms to satisfy the increasing demands for capacity and energy, and achieve a good video quality. In this paper, we propose a video quality-based framework for spectrum and energy efficient mobile association and resource allocation in heterogeneous wireless networks. The basic tenets of the framework are (1) two novel performance metrics, namely QSE and QEE, to capture spectrum usage and energy consumption from video quality’s perspective; and (2) a computationally efficient optimization model to derive mobile association and resource allocation for video connections in heterogeneous wireless networks. To this end, we first study the fundamental tradeoff between QSE and QEE in a PtP Rayleigh fading wireless channel. We then study QSE and QEE at the system level and develop a mobile association and resource allocation scheme that aims to jointly optimize system level QSE and QEE. The problem is formulated as a mixed-integer nonlinear optimization problem. Nonlinear fractional programming approach and dual decomposition method are applied to search the optimal solutions in a computationally efficient way. The simulation results evaluate the performance tradeoff between QSE and QEE, and show that the system performance, including PSNR distribution and maximum QSE/QEE values, greatly depends on bandwidth and power decaying factors. [ABSTRACT FROM AUTHOR]

Underwater video services could be a key application in the better scientific knowledge of the vast oceanic resources in our planet. However, limitations in the capacity of current available technology for underwater networks (UWSNs) raise the question of the feasibility of these services. When transmitting video, the main constraints are the limited bandwidth and the high propagation delays. At the same time the service performance depends on the needs of the target group. This paper considers the problems of estimations for the Mean Opinion Score (a standard quality measure) in UWSNs based on objective methods and addresses the topic of quality assessment in potential underwater video services from a subjective point of view. The experimental design and the results of a test planned according standardized psychometric methods are presented. The subjects used in the quality assessment test were ocean scientists. Video sequences were recorded in actual exploration expeditions and were processed to simulate conditions similar to those that might be found in UWSNs. Our experimental results show how videos are considered to be useful for scientific purposes even in very low bitrate conditions. [ABSTRACT FROM AUTHOR]

We study the influence of motion on the visibility of flicker distortions in naturalistic videos. A series of human subjective studies were executed to understand how motion silences the visibility of flicker distortions as a function of object motion, flicker frequency, and video quality. We found that flicker visibility is strongly reduced when the speed of coherent motion is large, and the effect is pronounced when video quality is poor. Based on this finding, we propose a model of flicker visibility on naturalistic videos. The target-related activation levels in the excitatory layer of neurons were estimated for a displayed video using a spatiotemporal backward masking model, and then the flicker visibility is predicted based on a learned model of neural flicker adaptation processes. Experimental results show that the prediction of flicker visibility using the proposed model correlates well with human perception of flicker distortions. We believe that sufficiently fast and coherent motion silences the perception of flicker distortions on naturalistic videos in agreement with a recently observed “motion silencing” effect on synthetic stimuli. We envision that the proposed model could be applied to develop perceptual video quality assessment algorithms that can predict “silenced” temporal distortions and account for them when computing quality judgments. [ABSTRACT FROM AUTHOR]

A high-definition video quality assessment (VQA) database that captures two typical video distortion types in video services (namely, “compression” and “compression followed by scaling”) is presented in this work. The VQA database, called MCL-V, contains 12 source video clips and 96 distorted video clips with subjective assessment scores. The source video clips are selected from a large pool of public-domain high-definition (HD) video sequences with representative and diversified contents. Both distortion types are perceptually adjusted to yield distinguishable distortion levels. An improved pairwise comparison method is adopted for subjective evaluation to save evaluation time. Several existing image and video quality assessment (IQA and VQA) algorithms are evaluated against the MCL-V database. The MCL-V database is publicly accessible in the link – http://mcl.usc.edu/mcl-v-database/ to facilitate future video quality assessment research of the community. [ABSTRACT FROM AUTHOR]

Advances in digital technology have allowed us to embed significant processing power in everyday video consumption devices. At the same time, we have placed high demands on the video content itself by continuing to increase spatial resolution while trying to limit the allocated file size and bandwidth as much as possible. The result is typically a trade-off between perceptual quality and fulfillment of technological limitations. To bring this trade-off to its optimum, it is necessary to understand better how people perceive video quality. In this work, we particularly focus on understanding how the spatial location of compression artifacts impacts visual quality perception, and specifically in relation with visual attention. In particular we investigate how changing the quality of the region of interest of a video affects its overall perceived quality, and we quantify the importance of the visual quality of the region of interest to the overall quality judgment. A three stage experiment was conducted where viewers were shown videos with different quality levels in different parts of the scene. By asking them to score the overall quality we found that the quality of the region of interest has 10 times more impact than the quality of the rest of the scene. These results are in line with similar effects observed in still images, yet in videos the relevance of the visual quality of the region of interest is twice as high than in images. The latter finding is directly relevant for the design of more accurate objective quality metrics for videos, that are based on the estimation of local distortion visibility. [ABSTRACT FROM AUTHOR]

Local backlight dimming is a technology aiming at both saving energy and improving visual quality on television sets. As the rendition of the image is specified locally, the numerical signal corresponding to the displayed image needs to be computed through a model of the display. This simulated signal can then be used as input to objective quality metrics. The focus of this paper is on determining which characteristics of locally backlit displays influence quality assessment. A subjective experiment assessing the quality of highly contrasted videos displayed with various local backlight-dimming algorithms is set up. Subjective results are then compared with both objective measures and objective quality metrics using different display models. The first analysis indicates that the most significant objective features are temporal variations, power consumption (probably representing leakage), and a contrast measure. The second analysis shows that modeling of leakage is necessary for objective quality assessment of sequences displayed with local backlight dimming. [ABSTRACT FROM AUTHOR]

Similar to previous video coding standards, transform and quantization are employed in the most recent video coding standard High Efficiency Video Coding (HEVC), and there exist a large number of transform coefficients being quantized to zeros. In order to reduce the computations in transform and quantization, a novel prediction algorithm is designed to early detect all-zero 4 × 4 blocks prior to transform and quantization. A theoretical analysis is performed on the 4 × 4 Discrete Cosine Transform (DCT) and the related quantization functions in HEVC; consequently, two sufficient conditions to early detect all-zero 4 × 4 blocks are derived and the proposed prediction algorithm is developed while balancing the detection efficiency and computational overheads. Experimental results demonstrate that the proposed algorithm is able to efficiently predict all-zero 4 × 4 blocks and reduce redundant DCT and quantization computations while maintaining the same coding performance in terms of video quality and bit rates. [ABSTRACT FROM AUTHOR]

It is now common to access mobile video services through various Internet-connectable devices such as game stations, smartphones and tablets. This paper proposes a new energy management scheme for mobile devices by making use of the temporal scalability available in scalable video coding (SVC) techniques. The proposed scheme determines a quality index for each video segment, based on the pixel-wise difference between the luminance planes of consecutive frames at each frame-rate, and predicts the computation needed to decode each frame using data relating frame sizes to previous decoding times. A new dynamic voltage and frequency scaling (DVFS) scheme is then invoked to achieve the specified quality index. The proposed scheme was implemented on a smartphone and a user test was conducted to assess the acceptability of different degree of frame-rate reduction. Real measurements showed that the proposed scheme uses between 5% and 41% less energy than the Linux DVFS governor used in many smartphones, while maintaining acceptable video quality. [ABSTRACT FROM AUTHOR]

Demand on multimedia content by consumers' handheld devices over wireless channels is on the increase. In view of the accelerated trend towards consumption of high quality video, power utilization by mobile devices is expected to excessively increase. Hence, it becomes equally important to advance more efficient power minimization techniques, in light of the short battery life in portable devices. However, power minimization algorithms that adopt consumers' perceptual quality of video have not received adequate research. This paper proposes a joint optimization of energy and quality requirements in a multiuser orthogonal frequencydivision multiplexing environment. A multi-objective optimization problem is formulated with the aim to identify bitrate allocations among users such that total power is minimized, and average quality is maximized. For this, a content-aware and energy-efficient resource allocation scheme (CaERAS) is proposed based on genetic and greedy algorithms. Simulation results show that CaERAS, as a lowcomplexity scheme, outperforms comparable methods in terms of efficiency and selectivity of suboptimal solutions. It is shown to acquire a suboptimal solution in as low as 0.0025 of the search space in previous methods. Also, a significant average saving of 85.66% in required energy is observed in broadcast transmission as opposed to unicast transmission. [ABSTRACT FROM AUTHOR]

Cooperatively fetching video content with the help of other mobile nodes can release some of the pressure on storage and bandwidth of the constrained mobile devices in wireless networks and speeds up the localization process of video resources so as to support better quality of viewing experience. In this context, the discovery of the appropriate mobile cooperative node becomes a key factor and is also a challenge for the deployment of such a fetching scheme. In this paper, we introduce a novel cooperative content fetching-based strategy to increase the quality of video delivery to mobile users in wireless networks (CCF). By intelligently monitoring the real-time variation in the state of the one-hop neighbors (immediate-neighbors) of the video resource downloader, CCF employs an innovative estimation model to measure the stability of these immediate-neighbors. In order to enhance the cooperative fetching efficiency, CCF designs a communication quality forecast model to measure link reliability and forecast the available bandwidth. By making use of a newly proposed cooperative fetching algorithm, CCF can speed up fetching and disseminating of video resources with the help of cooperative neighbors selected in terms of stability and communication quality. Simulation results show how CCF obtains higher selection accuracy of cooperative neighbors, lower average end-to-end delay, lower average packet loss ratio, higher average throughput, higher video quality, and lower maintenance overhead in comparison with state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

This paper presents recommended techniques for choosing video sequences for subjective experiments. Subjective video quality assessment is a well-understood field, yet scene selection is often driven by convenience or content availability. Three-dimensional testing is a newer field that requires new considerations for scene selection. The impact of experiment design on best practices for scene selection will also be considered. A semi-automatic selection process for content sets for subjective experiments will be proposed. [ABSTRACT FROM AUTHOR]

Video streaming via Transmission Control Protocol (TCP) networks has become a popular and highly demanded service, but its quality assessment in both objective and subjective terms has not been properly addressed. In this paper, based on statistical analysis a full analytic model of a no-reference objective metric, namely pause intensity (PI), for video quality assessment is presented. The model characterizes the video playout buffer behavior in connection with the network performance (throughput) and the video playout rate. This allows for instant quality measurement and control without requiring a reference video. PI specifically addresses the need for assessing the quality issue in terms of the continuity in the playout of TCP streaming videos, which cannot be properly measured by other objective metrics such as peak signal-to-noise-ratio, structural similarity, and buffer underrun or pause frequency. The performance of the analytical model is rigidly verified by simulation results and subjective tests using a range of video clips. It is demonstrated that PI is closely correlated with viewers' opinion scores regardless of the vastly different composition of individual elements, such as pause duration and pause frequency which jointly constitute this new quality metric. It is also shown that the correlation performance of PI is consistent and content independent. [ABSTRACT FROM AUTHOR]

Skype is one of the most popular video call services in the current Internet world. One of its strengths is the use of an adaptive mechanism to match the constraints of the underlying network. This work is focused on how this mechanism can maximize the video quality as perceived by the viewers using objective assessment methods. We built a testbed to stream certain video sequences through Skype between two clients over impaired communication channels. Original and recorded videos were compared to assess the achieved quality. Extensive experimentation has shown that Skype has problems when transmitting high motion videos and especially complex videos with frequent interchange between frames of low and high temporal information. The results suggest that random packet loss intensifies quality degradation for those videos more than packet loss bursts or jitter. [ABSTRACT FROM AUTHOR]

One promising solution to reduce the computational complexity of discrete cosine transform (DCT) is to identify the redundant computations and to get rid of them. In this study, the authors present a new method to predict zero‐quantised DCT coefficients for efficient implementation of intra‐frame video encoding by identifying such redundant computations. Traditional methods use the Gaussian statistical model of residual pixels to predict all‐zero or partial‐zero blocks. The proposed method is based on two key ideas. At first, the bounds of DCT coefficients are derived from the intermediate signals of the Loeffler DCT algorithm instead of calculating the sum of absolute difference (SAD) of residual pixels. The sufficiency conditions are then suitably chosen to predict the zero‐quantised coefficients to reduce the arithmetic complexity without degrading the video quality. Simulation results are found to validate the analytical model and show that the proposed prediction eliminates more redundant computations than the existing methods. Moreover, the authors have derived a pipelined VLSI architecture of the proposed prediction scheme which offers a saving of more than 63 and 91% of multiplications of the second stage of one‐dimensional DCT for high and low bit‐rate intra‐video encoding, respectively. [ABSTRACT FROM AUTHOR]

The digital video and multimedia content distributed via telecommunication networks suffers from several types of distortion or degradation during the process of acquisition, compression, processing, transmission and reproduction. One of the key factors in the success of a multimedia system or service is to achieve an acceptable quality of experience for the user, depending on the application. Video quality is one of the key factors in the overall quality of the experience perceived by users. For the purposes of measuring or estimating the video quality perceived by users, in the last decade various models have been proposed and there have been several standardizations. The "Video Quality Experts Group" (VQEG) is an organization that was created with the goal of advance the field of video quality assessment by investigating new and advanced subjective and objective quality metrics and measurement techniques. In this paper we review the work performed by the VQEG, the progress in this area and the recently completed models for estimating video quality. [ABSTRACT FROM AUTHOR]

There is a wide range of video services over complex transmission networks, and in some cases end users fail to receive an acceptable quality level. In this paper, the different factors that degrade users' quality of experience (QoE) in video streaming service that use TCP as transmission protocol are studied. In this specific service, impairment factors are: number of pauses, their duration and temporal location. In order to measure the effect that each temporal segment has in the overall video quality, subjective tests. Because current subjective test methodologies are not adequate to assess video streaming over TCP, some recommendations are provided here. At the application layer, a customized player is used to evaluate the behavior of player buffer, and consequently, the end user QoE. Video subjective test results demonstrate that there is a close correlation between application parameters and subjective scores. Based on this fact, a new metrics named VsQM is defined, which considers the importance of temporal location of pauses to assess the user QoE of video streaming service. A useful application scenario is also presented, in which the metrics proposed herein is used to improve video services. [ABSTRACT FROM AUTHOR]

Abstract: Rate control regulates the output bit rate of a video encoder in order to obtain optimum visual quality within the available network bandwidth and to maintain buffer fullness within a specified tolerance range. Due to the benefits of intra-only encoding, such as less computational cost and less latency, it has been more and more widely used. In this paper, we propose an accurate intra-only rate control scheme for H.264/AVC, which includes a novel complexity measurement and a new rate–distortion (R–D) model. We also propose a linear rate–complexity model which takes the intercept into consideration to reduce the estimation error. The proposed R–D model is integrated by the linear rate–complexity model and an exponential rate–quantization model. Based on theoretical analysis and experimental validation, the proposed scheme has high bits prediction precision, and it can also accurately handle buffer fullness. Compared with JVT-W042, our algorithm achieves higher average PSNR and improves the coding quality up to 0.35dB. [Copyright &y& Elsevier]

An approach to the direct measurement of perception of video quality change using electroencephalography (EEG) is presented. Subjects viewed 8-s video clips while their brain activity was registered using EEG. The video signal was either uncompressed at full length or changed from uncompressed to a lower quality level at a random time point. The distortions were introduced by a hybrid video codec. Subjects had to indicate whether they had perceived a quality change. In response to a quality change, a positive voltage change in EEG (the so-called P3 component) was observed at latency of about 400–600 ms for all subjects. The voltage change positively correlated with the magnitude of the video quality change, substantiating the P3 component as a graded neural index of the perception of video quality change within the presented paradigm. By applying machine learning techniques, we could classify on a single-trial basis whether a subject perceived a quality change. Interestingly, some video clips wherein changes were missed (i.e., not reported) by the subject were classified as quality changes, suggesting that the brain detected a change, although the subject did not press a button. In conclusion, abrupt changes of video quality give rise to specific components in the EEG that can be detected on a single-trial basis. Potentially, a neurotechnological approach to video assessment could lead to a more objective quantification of quality change detection, overcoming the limitations of subjective approaches (such as subjective bias and the requirement of an overt response). Furthermore, it allows for real-time applications wherein the brain response to a video clip is monitored while it is being viewed. [ABSTRACT FROM AUTHOR]

Visual quality of compressed video sequences depends on factors including spatial texture content and cognition-based factors such as prior knowledge and task in hand. The MOSp metric is a full reference objective quality metric which predicts perceived quality of sequences with video compression-induced impairments based on the spatial texture content and the mean squared error between original and compressed video sequences. In this paper, we extend the MOSp metric to incorporate cognition-based factors to identify regions in a video scene that attract human attention. The proposed metric has been tested on a variety of multimedia sequences of common intermediate format resolution compressed at a wide range of bitrates using the H.264/AVC coding standard. This metric shows a higher correlation with mean opinion score (MOS) than popular metrics, such as peak signal-to noise ratio, National Telecommunications and Information Administration/Institute for Telecommunication Sciences video quality metric, PSNRplus, and the Yonsei University metric. Results also show that by extending the MOSp metric to incorporate cognition-based factors such as skin information, its correlation with subjective scores (MOS) can be significantly improved in video content where humans are present. This algorithm is particularly useful for real-time quality estimation of multimedia sequences with block-based video compression-induced impairments because all the parameters of the metric can be calculated automatically with a modest amount of processing overhead. [ABSTRACT FROM AUTHOR]

Real-time, two-way transmission of American Sign Language (ASL) video over cellular networks provides natural communication among members of the Deaf community. As a communication tool, compressed ASL video must be evaluated according to the intelligibility of the conversation, not according to conventional definitions of video quality. Guided by linguistic principles and human perception of ASL, this paper proposes a full-reference computational model of intelligibility for ASL (CIM-ASL) that is suitable for evaluating compressed ASL video. The CIM-ASL measures distortions only in regions relevant for ASL communication, using spatial and temporal pooling mechanisms that vary the contribution of distortions according to their relative impact on the intelligibility of the compressed video. The model is trained and evaluating using ground truth experimental data collected in three separate studies. The CIM-ASL provides accurate estimates of subjective intelligibility and demonstrates statistically significant improvements over computational models traditionally used to estimate video quality. The CIM-ASL is incorporated into an H.264 compliant video coding framework, creating a closed-loop encoding system optimized explicitly for ASL intelligibility. The ASL-optimized encoder achieves bitrate reductions between 10% and 42%, without reducing intelligibility, when compared to a general purpose H.264 encoder. [ABSTRACT FROM AUTHOR]