The Consumer Digital Video Library [Best of the Web]

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%

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 sequences were obtained from open source movies, the Consumer Digital Video Library (CDVL) [38] and the Technical University of Munich (TUM). All sequences were in full 1080p HD resolution (1920 × 1080), with a frame rate for 25 f/s and a duration of exactly 10 s. An overview of the different source sequences is shown in Fig.…”

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 SRC are in the public domain. The entire T1A1 dataset is available for research and development purposes in the Consumer Digital Video Library (CDVL, www.cdvl.org, [4]). …”

Section: A T1a1mentioning

confidence: 99%

“…The intention was to anchor the subjective ratings from individual experiments onto the same perceptual scale. The T1A1 test divided the viewers and hypothetical reference circuits (HRCs) 4 into three overlapping subsets. All of the PVSs and ratings were combined without modification (i.e., treated as having been drawn from the larger pool of available PVSs and subjects).…”

Section: B Analyzing Multiple Subjective Datasets Simultaneouslymentioning

confidence: 99%

The history of video quality model validation

Pinson

2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP)

Webster

2013

Self Cite

Abstract-This paper describes objective video quality validation efforts conducted in the past two decades. Validation efforts to be examined include a validation test performed by the T1A1 committee in the early 1990's; five rounds of validation testing performed by the Video Quality Experts Group; and validation tests performed by ITU-T Study Group 12. Useful products that resulted from those efforts will be identified, including standards, datasets, and model validation techniques.