Mean Time Between Visible Artifacts in Visual Communications
MetadataShow full item record
As digital communication of television content becomes more pervasive, and as networks supporting such communication become increasingly diverse, the long-standing problem of assessing video quality by objective measurements becomes particularly important. Content owners as well as content distributors stand to benefit from rapid objective measurements that correlate well with subjective assessments, and further, do not depend on the availability of the original reference video. This thesis investigates different techniques of subjective and objective video evaluation. Our research recommends a functional quality metric called Mean Time Between Failures (MTBF) where failure refers to video artifacts deemed to be perceptually noticeable, and investigates objective measurements that correlate well with subjective evaluations of MTBF. Work has been done for determining the usefulness of some existing objective metric by noting their correlation with MTBF. The research also includes experimentation with network-induced artifacts, and a study on statistical methods for correlating candidate objective measurements with the subjective metric. The statistical significance and spread properties for the correlations are studied, and a comparison of subjective MTBF with the existing subjective measure of MOS is performed. These results suggest that MTBF has a direct and predictable relationship with MOS, and that they have similar variations across different viewers. The research is particularly concerned with the development of new no-reference objective metrics that are easy to compute in real time, as well as correlate better than current metrics with the intuitively appealing MTBF measure. The approach to obtaining greater subjective relevance has included the study of better spatial-temporal models for noise-masking and test data pooling in video perception. A new objective metric, 'Automatic Video Quality' metric (AVQ) is described and shown to be implemented in real time with a high degree of correlation with actual subjective scores, with the correlation values approaching the correlations of metrics that use full or partial reference. This is metric does not need any reference to the original video, and when used to display MPEG2 streams, calculates and indicates the video quality in terms of MTBF. Certain diagnostics like the amount of compression and network artifacts are also shown.