Name
Title | ||
|---|---|---|
Predicting the perceptual quality of networked video through light-weight bitstream analysis |
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 (QoE) requirements of users. This paper proposes an efficient and light-weight video quality prediction model through partial parsing of compressed video bitstreams. A set of features were introduced to reflect video content characteristics and distortions caused by compression and transmission. All the features can be obtained directly from the H.264/AVC compressed bitstream in parsing mode without decoding the pixel information in macroblocks. Based on these features, an artificial neural network model was trained for perceptual quality prediction. Evaluation results show that the proposed prediction model can achieve accurate prediction of perceptual video quality through low computation costs. Therefore, it is well-suited for real time networked video applications on embedded devices. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BlackSeaCom.2014.6849002 | Communications and Networking |
Keywords | Field | DocType |
data compression,decoding,embedded systems,neural nets,quality of experience,telecommunication traffic,video coding,h.264/avc compressed bitstream,qoe,artificial neural network model,embedded devices,lightweight bitstream analysis,lightweight video quality prediction model,networking protocols,parsing mode,perceptual video quality estimator,video traffic,wireless networked devices,predictive models,packet loss,computational modeling | Video processing,Computer science,Multiview Video Coding,Real-time computing,Video tracking,Video quality,Rate–distortion optimization,Video compression picture types,Scalable Video Coding,Uncompressed video | Conference |
ISSN | Citations | PageRank |
2375-8236 | 1 | 0.37 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdul Hameed | 1 | 190 | 13.33 |
| Rui Dai | 2 | 180 | 12.18 |
| Benjamin J. Balas | 3 | 229 | 14.65 |