A 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.

High-quality video is being increasingly delivered over Internet Protocol networks, which means that network operators and service providers need methods to measure the quality of experience (QoE) of the video services. In this paper, we propose a method to speed up the development of no-reference bitstream objective metrics for estimating QoE. This method uses full-reference objective metrics, which makes the process significantly faster and more convenient than using subjective tests. In this process, we have evaluated six publicly available full-reference objective metrics in three different databases, the EPFL-PoliMI database, the HDTV database, and the Live Video Wireless database, all containing transmission distortions in H.264 coded video. The objective metrics could be used to speed up the development process of no-reference real-time video QoE monitoring methods that are receiving great interest from the research community. We show statistically that the full-reference metric Video Quality Metric (VQM) performs best considering all the databases. In the EPFL-PoliMI database, SPATIAL MOVIE performed best and TEMPORAL MOVIE performed worst. When transmission distortions are evaluated, using the compressed video as the reference provides greater accuracy than using the uncompressed original video as the reference, at least for the studied metrics. Further, we use VQM to train a lightweight no-reference bitstream model, which uses the packet loss rate and the interval between instantaneous decoder refresh frames, both easily accessible in a video quality monitoring system.

Abstract-Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in order to create knowledge-rich environments populated by interconnected smart devices. In this paper we point out some problems of these previous initiatives and introduce SoaM (Smart Objects Awareness and Adaptation Model), an architecture for designing and seamlessly deploying web-powered context-aware semantic gadgets. Implementation and evaluation details of SoaM are also provided in order to identify future research challenges.

Since 1997, the Video Quality Experts Group (VQEG) has been active in the field of subjective and objective video quality assessment. The group has validated competitive quality metrics throughout several projects. Each of these projects requires mandatory actions such as creating a testplan and obtaining databases consisting of degraded video sequences with corresponding subjective quality ratings. Recently, VQEG started a new open initiative, the Joint Effort Group (JEG), for encouraging joint collaboration on all mandatory actions needed to validate video quality metrics. Within the JEG, effort is made to advance the field of both subjective and objective video quality measurement by providing proper software tools and subjective databases to the community. One of the subprojects of the JEG is the joint development of a hybrid H.264/AVC objective quality metric. In this paper, we introduce the JEG and provide an overview of the different ongoing activities within this newly started group.

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