A Model for Video Quality Assessment Considering Packet Loss for Broadcast Digital Television Coded in H.264
This paper presents a model to predict video quality perceived by the broadcast digital television (DTV) viewer. We present how noise on DTV can introduce individual transport stream (TS) packet losses at the receiver. The type of these errors is different than the produced on IP networks. Different...
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Published in: | International journal of digital multimedia broadcasting Vol. 2014; no. 2014; pp. 1 - 11 |
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Main Authors: | , |
Format: | Journal Article |
Language: | English |
Published: |
Cairo, Egypt
Hindawi Puplishing Corporation
01-01-2014
Hindawi Publishing Corporation Hindawi Limited |
Subjects: | |
Online Access: | Get full text |
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Summary: | This paper presents a model to predict video quality perceived by the broadcast digital television (DTV) viewer. We present how noise on DTV can introduce individual transport stream (TS) packet losses at the receiver. The type of these errors is different than the produced on IP networks. Different scenarios of TS packet loss are analyzed, including uniform and burst distributions. The results show that there is a high variability on the perceived quality for a given percentage of packet loss and type of error. This implies that there is practically no correlation between the type of error or the percentage of packets loss and the perceived degradation. A new metric is introduced, the weighted percentage of slice loss, which takes into account the affected slice type in each lost TS packet. We show that this metric is correlated with the video quality degradation. A novel parametric model for video quality estimation is proposed, designed, and verified based on the results of subjective tests in SD and HD. The results were compared to a standard model used in IP transmission scenarios. The proposed model improves Pearson Correlation and root mean square error between the subjective and the predicted MOS. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 1687-7578 1687-7586 |
DOI: | 10.1155/2014/242531 |