VIQID: a no-reference bit stream-based visual quality impairment detector

In order to ensure adequate quality towards the end users at all time, video service providers are getting more interested in monitoring their video streams. Objective video quality metrics provide a means of measuring (audio)visual quality in an automated manner. Unfortunately, most of the current existing metrics cannot be used for real-time monitoring due to their dependencies on the original video sequence. In this paper we present a new objective video quality metric which classifies packet loss as visible or invisible based on information extracted solely from the captured encoded H.264/AVC video bit stream. Our results show that the visibility of packet loss can be predicted with a high accuracy, without the need for deep packet inspection. This enables service providers to monitor quality in real-time

Comparing objective visual quality impairment detection in 2D and 3D video sequences

The skill level of teleoperator plays a key role in the telerobotic operation. However, plenty of experiments are required to evaluate the skill level in a conventional assessment. In this paper, a novel brain-based method of skill assessment is introduced, and the relationship between the teleoperator's brain states and skill level is first investigated based on a kernel canonical correlation analysis (KCCA) method. The skill of teleoperator (SoT) is defined by a statistic method using the cumulative probability function (CDF). Five indicators are extracted from the electroencephalo-graph (EEG) of the teleoperator to represent the brain states during the telerobotic operation. By using the KCCA algorithm in modeling the relationship between the SoT and the brain states, the correlation has been proved. During the telerobotic operation, the skill level of teleoperator can be well predicted through the brain states. © 2013 IEEE.Link_to_subscribed_fulltex

Video streaming

B

QoE for Mobile Streaming

No abstract