Quantifying the amount of spatial and temporal information in video test sequences

IEEE Trans. on Broadcast.

Wallendael

Crombecq

et al. 2012

Abstract-Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives.

Section: A Source Sequences Selection and Encodingmentioning

confidence: 99%

Section: A Source Sequences Selection and Encodingmentioning

confidence: 99%

No-Reference Bitstream-Based Visual Quality Impairment Detection for High Definition H.264/AVC Encoded Video Sequences

IEEE Trans. on Broadcast.

Wallendael

Crombecq

et al. 2012

“…These measurements are calculated frameby-frame and the maximum value is taken as overall SI and TI value for a particular video sequence. However, Ostaszewska et al [41] showed that the overall SI and TI of a video sequence can be better approximated by taking the upper quartile value instead of the maximum value as this eliminates the influence of peak values (caused by, e.g., scene cuts). Hence, the SI and TI value of a video sequence were calculated as follows:…”

Section: A Source Video Sequence Selectionmentioning

confidence: 99%

Constructing a No-Reference H.264/AVC Bitstream-Based Video Quality Metric Using Genetic Programming-Based Symbolic Regression

IEEE Trans. Circuits Syst. Video Technol.

Deschrijver

Vladislavleva³

et al. 2013

Abstract-In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel noreference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream.Index Terms-H.264/AVC, high definition, no-reference, objective video quality metric, quality of experience (QoE).

“…These sequences were taken from open source movies, the Consumer Digital Video Library (CDVL) [5] and the Technical University of Munich (TUM). However, instead of taking the maximum SI and TI value over all the video frames, the upper quartile (Q3) value is taken as overall value for the sequence, as recommended by Ostaszewska et al [6]. In Figure 2, a screenshot is presented of the eight selected video sequences.…”

Section: Source Video Sequencesmentioning

confidence: 99%

High definition H.264/AVC subjective video database for evaluating the influence of slice losses on quality perception

2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX)

Wallendael

Walle

et al. 2013

Prior to the construction or validation of objective video quality metrics, ground-truth data must be collected by means of a subjective video database. This database consists of (impaired) video sequences and corresponding subjective quality ratings. However, creating this subjective database is a timeconsuming and expensive task. There is an ongoing effort towards publishing such subjective video databases into the public domain. This facilitates the development of new objective quality metrics. In this paper, we present a new subjective video database consisting of impaired High Definition H.264/AVC encoded video sequences and associated quality ratings gathered from a subjective experiment. This database can be used freely to determine impairment visibility or estimate overall quality of a video in the case of lost slices due to network impairments.Index Terms-Video quality assessment, Subjective video quality, Objective video quality metric, Quality of Experience, Quality of Service