No-reference Video Quality Metric Research Articles

Analyzing the effect of adding temporal features to an autoencoder-based video quality model

According to Cisco, most Internet traffic is currently comprised of videos. Therefore, developing a quality assessment method for assuring that those videos are received and displayed with quality at the user side is an important and challenging task. As a consequence, over the last decades, several no-reference video quality metrics have been proposed with the goal of blindly predicting (with no access to the original signal) the quality of videos in streaming applications. One of such metrics is NAVE, whose architecture includes an auto-encoder module that produces a compact set of visual features with a higher descriptive capacity. Nevertheless, the visual features in NAVE do not include descriptive temporal features that are sensitive to temporal degradation. In this work, we analyze the effect on accuracy performance of using a new type of temporal features, based on natural scene statistics. This approach has the goal of making the tested video quality metric more generic, i.e. sensitive to both spatial and temporal distortions and therefore adequate for video streaming applications.

No-reference artifacts measurements based video quality metric

In multimedia delivery, perceived quality of video signal has a significant role in overall users Quality of Experience (QoE). Therefore, multimedia service providers must constantly measure and monitor perceived video quality, which is usually performed using no-reference (NR) video quality metrics. In this paper, a novel NR objective video quality metric named Artifacts Measurements Based Video Quality Metric (AMB-VQM) is proposed. In addition to artifacts measures computed by blocking (BL), packet-loss (PL), and freezing (FZ) artifacts detection algorithms, the metric incorporates artifacts masking based on spatial and temporal video content complexity. Furthermore, a newly created FERIT-RTRK-2 video quality database, which contains 486 Full HD test video sequences impaired by video compression (MPEG-2, H.264/AVC, and H.265), packet-loss, frame freezing, and combinations of these procedures, is presented in this paper. FERIT-RTRK-2 database is publicly available for scientific community at http://www.rt-rk.com/other/VideoDBReadme.html. Additionally, newly created user-friendly subjective video quality assessment tool with graphical user interface, which can be used for conducting of subjective video quality experiments, is presented. In experimental part the performance of the proposed AMB-VQM is compared to this of 10 other objective video quality metrics (PSNR, SSIM, VSNR, PSNRHVS, PSNRHVSM, VIFP, ViS3, ST-MAD, BRISQUE, VIIDEO) using distorted video sequences from four different video quality databases: CSIQ, LIVE, FERIT-RTRK and FERIT-RTRK-2. The results show that AMB-VQM achieves high performance when predicting the video quality for videos distorted in different manners. AMB-VQM results outperform the results of most of the analyzed popular and very often used video quality metrics.

No-Reference Video Quality Metrics Using Wavelet Transform with Several Noises

This paper presents a blind assessment of video quality called the no-reference video quality metric. The proposed scheme used video watermarking that involves 8x8 blocks of Haar and Daubechies (D4) wavelet transform within the RGB domain. However, several noise types have experimented, those are salt-and-pepper, Gaussian blur, Gaussian and distort ripple. In addition, JPEG compression has been tested within the proposed method. In this paper, a robust scheme is proposed against the attacks of swapping, frame dropping, statistical analysis and averaging. The experimental results of the proposed system give acceptable outcomes. The Daubechies4 filter has given a better result than Haar filter. The perceived video quality metric has been performed through Peak Signal to Noise Ratio (PSNR) and Root Mean Square (MSE) metric. Daubechies4 has given the best result as compared to Haar filter and Discrete Cosine Transform (DCT).

A Novel No-Reference Metric for Estimating the Impact of Frame Freezing Artifacts on Perceptual Quality of Streamed Videos

Online monitoring of multimedia networks is required to ensure seamless and ubiquitous delivery of services to the end users. Quality of multimedia content, such as video streams, often gets degraded due to network losses such as packet loss. Frame freezing artifacts are introduced in a video stream when packet loss or packet delay takes place. Estimating the perceptual impact of these artifacts on quality of experience of end users helps service providers to maintain quality of service. In this paper, we have presented a novel no-reference video quality metric, which measures the impact of frame freezing due to packet loss and delay in video streaming networks. The proposed metric is based on several features that directly impact the quality of experience of end users. These features, including motion characteristics of videos, are calculated using the temporal information between video frames and then combined mathematically to form a video quality metric. Different weights are assigned to different features for better performance of the proposed metric. With detailed experiments, we have shown that our method outperforms other contemporary methods in terms of high accuracy and low computation time in frame freeze detection, low root mean square values, high coefficient of determination, and high correlation between subjective and objective measurements. We have used five video databases for our model's evaluation and validation. Furthermore, we have shown that our method is statistically superior to the other models in comparison.

Video Quality Metric for Streaming Service Using DASH Standard

Currently, dynamic adaptive streaming over HTTP (DASH) standard is used to change the video resolution according to the network capacity of the end user’s. However, if the video resolution changes frequently, the attention of the users can be affected decreasing their quality-of-experience (QoE). In this context, this paper proposes a no-reference video quality metric that takes three impairment factors into account, initial buffering delay, temporal interruptions or pauses, and video resolution changes during a video transmission. Also, the temporal locations of pauses and resolution changes are considered. In order to perform this task, extensive subjective tests were conducted. Experimental results show that the evaluators’ QoE is highly correlated with the frequency of pauses, video resolution changes and initial delay. Based on these results a video streaming quality metric for DASH services (VsQM $_{\text {DASH}}$ ) is formulated and implemented on PC and mobile hand-held devices. Finally, the performance of VsQM $_{\text {DASH}}$ is validated with additional subjective tests reaching a person correlation coefficient of 0.99. Furthermore, experimental results demonstrated that the proposed metric has low complexity because it is based on application level parameters and its energy consumption is negligible in relation to the energy consumed by the video player; therefore, can be easily implemented in consumer electronic devices.

Evaluating the influence of packet loss on visual quality of perception for high bandwidth automotive networks

This paper presents a new approach for modelling the perceptual quality of automotive video corrupted by packet loss. The model uses both temporal and spatial image statistics to distinguish between salient and non-salient packet loss events and hence derive an objective no-reference video quality metric. A new automotive specific video quality database is presented, consisting of 50 video sequences with associated human saliency data and mean opinion scores. Experiments carried out on this dataset investigate the influence of packet loss impairments on the visual attention of the viewer. The results suggest that packet loss impairments do not significantly alter visual attention. This is an important result, since it suggests that in the context of automotive vision, the visual attention of the driver is more strongly influenced by task related factors such as pedestrian detection, than by low-level sensory cues. The experiments further demonstrate that the proposed quality model correlates closely with human opinion for videos corrupted with network induced packet loss using a hybrid automotive testbed network simulation.

A Novel No-Reference Video Quality Metric for Evaluating Temporal Jerkiness due to Frame Freezing

In this work, we propose a novel no-reference (NR) video quality metric that evaluates the impact of frame freezing due to either packet loss or late arrival. Our metric uses a trained neural network acting on features that are chosen to capture the impact of frame freezing on the perceived quality. The considered features include the number of freezes, freeze duration statistics, inter-freeze distance statistics, frame difference before and after the freeze, normal frame difference, and the ratio of them. We use the neural network to find the mapping between features and subjective test scores. We optimize the network structure and the feature selection through a cross validation procedure, using training samples extracted from both VQEG and LIVE video databases. The resulting feature set and network structure yields accurate quality prediction for both the training data containing 54 test videos and a separate testing dataset including 14 videos, with Pearson Correlation Coefficients greater than 0.9 and 0.8 for the training set and the testing set, respectively. Our proposed metric has low complexity and could be utilized in a system with realtime processing constraint.

No reference quality assessment for MPEG video delivery over IP

Video delivering over Internet protocol (IP)-based communication networks is widely used in the actual information sharing scenario. As well known, the best-effort Internet architecture cannot guarantee an errorless data delivering. In this paper, an objective no-reference video quality metric for assessing the impact of the degradations introduced by video transmission over heterogeneous IP networks is presented. The proposed approach is based on the analysis of the inter-frame correlation measured at the output of the rendering application. It does not require any information on errors, delays, and latencies affecting the links and on the countermeasures introduced by decoders in order to face the potential quality loss. Experimental results show the effectiveness of the proposed algorithm in approximating the assessments obtained by using human visual system (HVS)-inspired full reference metrics.

Toward Deployable Methods for Assessment of Quality for Scalable IPTV Services

Scalable video coding (H.264 SVC) is an attractive option for video service providers due to its ability to adapt a video's bitstream at the server to suit different network conditions and device characteristics. Lowering a video's bitrate can be achieved through reductions in frame rate, spatial resolution, and/or by increasing the quantization levels applied to the video sequence. In this paper, we first evaluate the effects of such scalability using some full-reference and no-reference video quality metrics, namely PSNR, SSIM, blocking, and blurring. No-reference metrics have the ability to capture the degradation in video quality caused by employing scalability in one or more dimensions. We study if conclusions drawn in previous works, which are based on well-known test video content, hold true for real-world broadcast content. We then discuss how, using these results for a particular content type, the use of no-reference metrics can be enabled in place of, or to supplement, existing widely used full-reference quality assessment metrics. We conduct an experimental analysis by transmitting video encoded at different scalability points over a lossy network to ascertain the effect of loss when scalability is employed in one or more dimensions. We analyze these results using a reduced reference metric called delta-blocking, which can detect visual damage of frames that causes a decrease in a user's quality of experience when perceived by the user. If the levels of packet loss are excessively high, this can lead the decoder to drop some video frames. To combat this type of frame loss, we propose a simple windowing algorithm that can automatically re-align the corresponding values for reduced-reference comparison, allowing for video quality monitoring to continue.

No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream

A no-reference (NR) quality measure for networked video is introduced using information extracted from the compressed bit stream without resorting to complete video decoding. This NR video quality assessment measure accounts for three key factors which affect the overall perceived picture quality of networked video, namely, picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects of the human visual system. First, the picture quality in the spatial domain is measured, for each frame, relative to quantization under an error-free transmission condition. Second, picture quality is evaluated with respect to packet loss and the subsequent error propagation. The video frame quality in the spatial domain is, therefore, jointly determined by coding distortion and packet loss. Third, a pooling scheme is devised as the last step of the proposed quality measure to capture the perceived quality degradation in the temporal domain. The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.

Rule-Based No-Reference Video Quality Evaluation Using Additionally Coded Videos

This contribution presents a no-reference video quality metric, which is based on a set of simple rules that assigns a given video to one of four different content classes. The four content classes distinguish between video sequences which are coded with a very low data rate, which are sensitive to blocking effects, which are sensitive to blurring, and a general model for all other types of video sequences. The appropriate class for a given video sequence is selected based on the evaluation of feature values of an additional low-quality version of the given video, which is generated by encoding. The visual quality for a video sequence is estimated using a set of features, which includes measures for the blockiness, the blurriness, the spatial activity, and a set of additional continuity features. The way these features are combined to compute one overall quality value is determined by the feature class, to which the video has been assigned. We also propose an additional correction step for the visual quality value. The proposed metric is verified in a process, which includes visual quality values originating from subjective quality tests in combination with a cross validation approach. The presented metric significantly outperforms peak-signal-to-noise ratio as a visual quality estimator. The Pearson correlation between the estimated visual quality values and the subjective test results takes on values as high as 0.82.