Video Quality Assessment Database Research Articles

Temporal Video Quality Model Accounting for Variable Frame Delay Distortions

We announce a new video quality model (VQM) that accounts for the perceptual impact of variable frame delays (VFD) in videos with demonstrated top performance on the laboratory for image and video engineering (LIVE) mobile video quality assessment (VQA) database. This model, called VQM_VFD, uses perceptual features extracted from spatial-temporal blocks spanning fixed angular extents and a long edge detection filter. VQM_VFD predicts video quality by measuring multiple frame delays using perception based parameters to track subjective quality over time. In the performance analysis of VQM_VFD, we evaluated its efficacy at predicting human opinions of visual quality. A detailed correlation analysis and statistical hypothesis testing show that VQM_VFD accurately predicts human subjective judgments and substantially outperforms top-performing image quality assessment and VQA models previously tested on the LIVE mobile VQA database. VQM_VFD achieved the best performance on the mobile and tablet studies of the LIVE mobile VQA database for simulated compression, wireless packet-loss, and rate adaptation, but not for temporal dynamics. These results validate the new model and warrant a hard release of the VQM_VFD algorithm. It is freely available for any purpose, commercial, or noncommercial at http://www.its.bldrdoc.gov/vqm/ .

QoE-aware video streaming for SVC over multiuser MIMO–OFDM systems

We propose a QoE-aware video streaming solution to maximize multiuser QoE for Scalable Video Coding (SVC) streaming over multiuser (MU) Multiple-Input Multiple-Output (MIMO)–Orthogonal Frequency Division Multiplexing (OFDM) systems. We achieve it by integrating novel QoE-aware video adaptation (QoEVA) and QoE-aware resource allocation (QoERA) schemes. We first study QoEVA of SVC via a subjective video quality assessment database and derive QoE-optimized scalability adaptation tracks. A rate-QoE model is then constructed to approximate the track and is employed to design QoERA. By proving the NP-hardness of the QoERA problem, we propose an adaptive solution where resource block assignment, power allocation and modulation selection are jointly optimized to enhance multiuser QoE. Experimental results show that the proposed QoEVA significantly performs better than the conventional video adaptation schemes and the proposed QoERA achieves much better user experience when compared to state-of-the-art solutions. Our solution can be employed for both pre-coded and live video streaming.

Blind prediction of natural video quality.

We propose a blind (no reference or NR) video quality evaluation model that is nondistortion specific. The approach relies on a spatio-temporal model of video scenes in the discrete cosine transform domain, and on a model that characterizes the type of motion occurring in the scenes, to predict video quality. We use the models to define video statistics and perceptual features that are the basis of a video quality assessment (VQA) algorithm that does not require the presence of a pristine video to compare against in order to predict a perceptual quality score. The contributions of this paper are threefold. 1) We propose a spatio-temporal natural scene statistics (NSS) model for videos. 2) We propose a motion model that quantifies motion coherency in video scenes. 3) We show that the proposed NSS and motion coherency models are appropriate for quality assessment of videos, and we utilize them to design a blind VQA algorithm that correlates highly with human judgments of quality. The proposed algorithm, called video BLIINDS, is tested on the LIVE VQA database and on the EPFL-PoliMi video database and shown to perform close to the level of top performing reduced and full reference VQA algorithms.

Evaluating the Role of Content in Subjective Video Quality Assessment

Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account. In this paper, we aim to identify and analyze differences between human cognitive, affective, and conative responses to a set of videos commonly used for VQA and a set of videos specifically chosen to include video content which might affect the judgment of evaluators when perceived video quality is in question. Our findings indicate that considerable differences exist between the two sets on selected factors, which leads us to conclude that videos starring a different type of content than the currently employed ones might be more appropriate for VQA.

Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing

We present a family of reduced reference video quality assessment (QA) models that utilize spatial and temporal entropic differences. We adopt a hybrid approach of combining statistical models and perceptual principles to design QA algorithms. A Gaussian scale mixture model for the wavelet coefficients of frames and frame differences is used to measure the amount of spatial and temporal information differences between the reference and distorted videos, respectively. The spatial and temporal information differences are combined to obtain the spatio-temporal-reduced reference entropic differences. The algorithms are flexible in terms of the amount of side information required from the reference that can range between a single scalar per frame and the entire reference information. The spatio-temporal entropic differences are shown to correlate quite well with human judgments of quality, as demonstrated by experiments on the LIVE video quality assessment database.

Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

We introduce a new video quality database that models video distortions in heavily-trafficked wireless networks and that contains measurements of human subjective impressions of the quality of videos. The new LIVE Mobile Video Quality Assessment (VQA) database consists of 200 distorted videos created from 10 RAW HD reference videos, obtained using a RED ONE digital cinematographic camera. While the LIVE Mobile VQA database includes distortions that have been previously studied such as compression and wireless packet-loss, it also incorporates dynamically varying distortions that change as a function of time, such as frame-freezes and temporally varying compression rates. In this article, we describe the construction of the database and detail the human study that was performed on mobile phones and tablets in order to gauge the human perception of quality on mobile devices. The subjective study portion of the database includes both the differential mean opinion scores (DMOS) computed from the ratings that the subjects provided at the end of each video clip, as well as the continuous temporal scores that the subjects recorded as they viewed the video. The study involved over 50 subjects and resulted in 5,300 summary subjective scores and time-sampled subjective traces of quality. In the behavioral portion of the article we analyze human opinion using statistical techniques, and also study a variety of models of temporal pooling that may reflect strategies that the subjects used to make the final decision on video quality. Further, we compare the quality ratings obtained from the tablet and the mobile phone studies in order to study the impact of these different display modes on quality. We also evaluate several objective image and video quality assessment (IQA/VQA) algorithms with regards to their efficacy in predicting visual quality. A detailed correlation analysis and statistical hypothesis testing is carried out. Our general conclusion is that existing VQA algorithms are not well-equipped to handle distortions that vary over time. The LIVE Mobile VQA database, along with the subject DMOS and the continuous temporal scores is being made available to researchers in the field of VQA at no cost in order to further research in the area of video quality assessment.

Video Quality Assessment Based on Measuring Perceptual Noise From Spatial and Temporal Perspectives

Video quality assessment (VQA) exploits important properties of the sophisticated human visual system (HVS). In this paper, we study a series of fundamental HVS characteristics for subjective video quality assessment, and incorporate them into a systematic framework to simulate subjective evaluation on impaired videos. Based on this framework, we develop a novel full-reference metric, namely, perceptual quality index (PQI). Specifically, the proposed PQI metric comprises four major modules: 1) visual performance equation for the foveal and extra-foveal vision based on the cortical magnification theory; 2) perceptible noise detection using a spatial-temporal just noticeable difference model, and its quantification in both spatial and temporal channels, considering the varying error sensitivity due to the contrast and motion masking effects; 3) instantaneous error summation with inhibition of weak local distortions, and quality degradation accumulation over time that models the visual persistence and recency effect; and 4) fusion of the spatial and temporal noise intensities into a perceptual quality index. Compared with some state-of-the-art VQA models, the PQI metric, which exploits multiple visual properties, measures video quality more accurately and reliably on two VQA databases.