Browsing by Author "Brunnstrom, Kjell"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Application of full-reference video quality metrics in IPTV(IEEE, 2017-07-20) Sedano, Inigo; Prieto, Gorka; Brunnstrom, Kjell; Kihl, Maria; Montalban, Jon; FACTORYExecuting an accurate full-reference metric such as VQM can take minutes in an average computer for just one user. Therefore, it can be unfeasible to analyze all the videos received by users in an IPTV network for example consisting of 10.000 users using a single computer running the VQM metric. One solution can be to use a lightweight no-reference metrics in addition to the full-reference metric mentioned. Lightweight no-reference metrics can be used for discarding potential situations to evaluate because they are accurate enough for that task, and then the full-reference metric VQM can be used when more accuracy is needed. The work in this paper is focused on determining the maximum number of situations/users that can be analyzed simultaneously using the VQM metric in a computer with good performance. The full-reference metric is applied on the transmitter using a method specified in the recommendation ITU BT.1789. The best performance achieved was 112.8 seconds per process.Item Hybrid video quality prediction: reviewing video quality measurement for widening application scope: reviewing video quality measurement for widening application scope(2015-01) Barkowsky, Marcus; Sedano, Inigo; Brunnstrom, Kjell; Leszczuk, Mikolaj; Staelens, Nicolas; Leszczuk, Mikołaj; FACTORYA tremendous number of objective video quality measurement algorithms have been developed during the last two decades. Most of them either measure a very limited aspect of the perceived video quality or they measure broad ranges of quality with limited prediction accuracy. This paper lists several perceptual artifacts that may be computationally measured in an isolated algorithm and some of the modeling approaches that have been proposed to predict the resulting quality from those algorithms. These algorithms usually have a very limited application scope but have been verified carefully. The paper continues with a review of some standardized and well-known video quality measurement algorithms that are meant for a wide range of applications, thus have a larger scope. Their individual artifacts prediction accuracy is usually lower but some of them were validated to perform sufficiently well for standardization. Several difficulties and shortcomings in developing a general purpose model with high prediction performance are identified such as a common objective quality scale or the behavior of individual indicators when confronted with stimuli that are out of their prediction scope. The paper concludes with a systematic framework approach to tackle the development of a hybrid video quality measurement in a joint research collaboration.