Quality of Experience Sample Clauses
Quality of Experience. Serving public sector organizations has been an integral part of North Highland since the firm’s inception nearly 30 years ago. Over the last five years alone, North Highland delivered more than 1,100 projects for public sector agencies. We are proud of this track record, and have achieved it because of our team’s consistent delivery of the highest quality services. The outstanding quality of our experience, and in turn the value we bring to our public sector clients, is demonstrated through the expertise of our people, our comprehensive set of public sector management consulting service offerings, and perhaps most importantly, our excellent rate of repeat business among clients.
Quality of Experience. We recognise that the trajectory of each town and urban district is different. There are different types of place, with different functions. We agree to support the enhancement quality of experience for people in each type of town and urban district, informing strategies around the blend of services, amenities and design quality.
Quality of Experience. In Section 7 is devoted to the definition and discussion of the Quality of Experience (QoE). QoE is a subjective measure of a user’s experiences with a service. In the con- text of network-aware P2P video distribution networks, QoE evaluation could give the peers and nodes (and, as a consequence, the service providers and network operators) the capability to optimize network resources. All best known approaches to QoE evaluation can be broadly classified into the fol- lowing areas: direct comparison, subjective quality evaluations, loss-distortion models. From our review of those approaches, it appears that this kind of evaluation is a matter of objective and subjective procedures, and all researches indicate that video quality es- timates depend on the specific characteristics of individual videos and that the human factor plays a major role into the overall algorithm calibration. This mandatory leads to the design of models which are to be fine-tuned by means of averaged subjective inputs from human observers assessing some levels of reference quality for the streams. Our main conclusion is that all models that succeeded in removing the model’s dependencies on specific characteristics of the video streams, must be calibrated by a panel of human observers, who are needed to tune a mapping function between network loss-related parameters and acceptable subjective quality: taking the video out of the equation can only be done by bringing the user into it! This calibration effort is to be done only once at bootstrap time, and the mapping seems to be simple in terms of mathematical expression (monotonic in both the re- viewed rPSNR and PSQA approaches), and its sensitivity to the set of human subjects employed to derive it is quite low, since similar results where obtained by different research groups under much different conditions. Thus a safe incremental approach can be taken in our project in the design of a real-time QoE evaluation module.