| dc.description.abstract | With the exponential growth of video traffic over wireless networked and embedded
devices such as mobile phones and sensors, mechanisms are needed to predict the perceptual
quality of video in real time and with low complexity, based on which networking protocols can
control video quality and optimize network resources to meet the quality of experience
requirements of users. This thesis is composed of three related pieces of work. In the first piece of
work, an efficient and light-weight video quality prediction model through partial parsing of
compressed from the H.264/AVC compressed bitstream is proposed. A set of features were
introduced to reflect video content characteristics and distortions caused by compression and
transmission and were obtained directly in parsing mode without decoding the pixel information
in macro-blocks. Based on the features, an artificial neural network model was trained for
perceptual quality prediction. In the second piece of work, a perceptual video quality prediction
model is trained based on massive subjective test results. Prediction of perceptual quality is
achieved through a decision tree using a set of easily calculated features from the compressed
bitstream and the network. Moreover, based on the prediction model, a novel Forward Error
Correction (FEC) scheme is introduced to protect video packets by taking into consideration video
content characteristics, compression parameters, as well as network condition. Given a perceptual
quality requirement, the error control scheme adjusts the level of protection for different
components in a video stream such that the network overhead needed for transmission is
minimized. In the third piece of work, a study was conducted to examine whether the previous
prediction model could provide a good confidence measure in a different domain of judgments.
The accuracy of judgements demonstrated the predictive validity of confidence measure with
respect packet loss ratio traits. The results of this study were consistent with the previous one and the experiments suggested that brief and evaluative thin slice judgments are made relatively
intuitively. Present research represents a new entry into the domain of high level judgments, such
as video confidence measure by the use of our existing perception quality model. | en_US |
