We have developed reduced reference parametric models for estimating perceived quality in audiovisual multimedia services. We have created 144 unique configurations for audiovisual content including various application and network parameters such as bitrates and distortions in terms of bandwidth, packet loss rate and jitter. To generate the data needed for model training and validation we have tasked 24 subjects, in a controlled environment, to rate the overall audiovisual quality on the absolute category rating (ACR) 5-level quality scale. We have developed models using Random Forest and Neural Network based machine learning methods in order to estimate Mean Opinion Scores (MOS) values. We have used information retrieved from the packet headers and side information provided as network parameters for model training. Random Forest based models have performed better in terms of Root Mean Square Error (RMSE) and Pearson correlation coefficient. The side information proved to be very effective in developing the model. We have found that, while the model performance might be improved by replacing the side information with more accurate bit stream level measurements, they are performing well in estimating perceived quality in audiovisual multimedia services.
In 2010 @cite_0 conducted subjective experiments to explore methods to predict audiovisual quality objectively for video calls in wireless applications. They have presented subjective test results for 60 test conditions on how audio and video contribute to overall audiovisual quality and develop models to reflect this relationship, and investigated how network and application parameters affect overall audiovisual quality. In their analysis they have used a regression model to predict audiovisual quality from packet loss rate and frame rate.
@cite_0
2137870642 2766773582 2276285412 2074883210
Mobile wireless multimedia applications (e.g., video calls and IPTV) have gained great momentum in recent years. An important issue is to monitor predict overall audiovisual quality, instead of audio-only or video-only quality, non-intrusively for technical or commercial reasons. Previous audiovisual modeling research mainly considered application parameters (e.g., codec and send bit rate). Little attention has been paid to how network parameters, e.g., Packet Error Rate (PER) affect audiovisual quality. The aim of this paper is to explore methods to predict audiovisual quality objectively for video calls in wireless applications. The contributions of the paper are twofold. Firstly, we present subjective test results on how audio and video contribute to overall audiovisual quality and develop models to reflect this relationship. Secondly, we investigated how network parameters (e.g., PER) and application parameters, e.g., video Frame Rate (FR) affect overall audiovisual quality. We developed a regression model to predict audiovisual quality from PER and FR which can be used to monitor predict audiovisual quality non-intrusively. We also explore the possibility to predict audiovisual quality from full-reference voice and video quality metrics (i.e., PESQ and PSNR) and from combined PESQ PSNR and network application parameters. The different predication accuracy obtained from these models (accuracy from 84 to 93 ) indicates the complex attributes in audiovisual quality prediction. An extended Evalvid NS-2 platform is developed to support simulation of video calls over wireless networks. Over-the-top mobile adaptive video streaming is invariably influenced by volatile network conditions, which can cause playback interruptions (stalling or rebuffering events) and bitrate fluctuations, thereby impairing users’ quality of experience (QoE). Video quality assessment models that can accurately predict users’ QoE under such volatile network conditions are rapidly gaining attention, since these methods could enable more efficient design of quality control protocols for media-driven services such as YouTube, Amazon, Netflix, and many others. However, the development of improved QoE prediction models requires data sets of videos afflicted with diverse stalling events that have been labeled with ground-truth subjective opinion scores. Toward this end, we have created a new mobile video quality database that we call LIVE Mobile Stall Video Database-II. Our database contains a total of 174 videos afflicted with distortions caused by 26 different stalling patterns. We describe the way we simulated the diverse stalling events to create a corpus of distorted videos, and we detail the human study we conducted to obtain continuous-time subjective scores from 54 subjects. We also present the outcomes of our comprehensive analysis of the impact of several factors that influence subjective QoE, and report the performance of existing QoE-prediction models on our data set. We are making the database (videos, subjective data, and video metadata) publicly available in order to help the advance state-of-the-art research on user-centric mobile network planning and management. The database may be accessed at http: live.ece.utexas.edu research LIVEStallStudy liveMobile.html . Quality of experience (QoE) has significant influence on whether or not a user will choose a service or product in the competitive era. For multimedia services, there are various factors in a communication ecosystem working together on users, which stimulate their different senses inducing multidimensional perceptions of the services, and inevitably increase the difficulty in measurement and estimation of the user's QoE. In this paper, a user-centric objective QoE evaluation model (QAVIC model for short) is proposed to estimate the user's overall QoE for audiovisual services, which takes account of perceptual audiovisual quality (QAV) and user interest in audiovisual content (IC) amongst influencing factors on QoE such as technology, content, context, and user in the communication ecosystem. To predict the user interest, a number of general viewing behaviors are considered to formulate the IC evaluation model. Subjective tests have been conducted for training and validation of the QAVIC model. The experimental results show that the proposed QAVIC model can estimate the user's QoE reasonably accurately using a 5-point scale absolute category rating scheme. It is generally recognized that severe video distortions that are transient in space and or time have a large effect on overall perceived video quality. In order to understand this phenomena, we study the distribution of spatio-temporally local quality scores obtained from several video quality assessment (VQA) algorithms on videos suffering from compression and lossy transmission over communication channels. We propose a content adaptive spatial and temporal pooling strategy based on the observed distribution. Our method adaptively emphasizes “worst” scores along both the spatial and temporal dimensions of a video sequence and also considers the perceptual effect of large-area cohesive motion flow such as egomotion. We demonstrate the efficacy of the method by testing it using three different VQA algorithms on the LIVE Video Quality database and the EPFL-PoliMI video quality database.