@inproceedings{Arun2016a,
abstract = {In this paper we propose a new quality metric to estimate the impact of packet loss on the perceptual quality of encoded video sequences transmitted over error-prone networks. The proposed metric, henceforth referred to as Cumulative Distortion using Structural Similarity (CDSSIM), quantifies the overall structural distortion resulting from bidirectional error propagation in predictively coded, motion compensated videos. Furthermore, we present a No-Reference (NR) sparse regression model to predict the proposed CDSSIM metric using pre-defined features associated with slice loss. The Least Absolute Shrinkage and Selection Operator (LASSO) method is applied for two resolution formats with features extracted solely from the encoded bit-stream. Standardized statistical performance measures show that the model can predict the cumulative distortion to a high degree of accuracy. We further evaluate the results using a Quartile-Based Prioritization (QBP) scheme and demonstrate that the predicted data provides an effective way to prioritize packets for video streaming applications.},
author = {Sankisa, Arun and Pandremmenou, Katerina and Kondi, Lisimachos P. and Katsaggelos, Aggelos K.},
booktitle = {2016 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2016.7532728},
isbn = {978-1-4673-9961-6},
issn = {15224880},
keywords = {Cumulative distortion,LASSO,Packet prioritization,Structural Similarity,Video quality},
month = {sep},
pages = {2097--2101},
publisher = {IEEE},
title = {{A novel cumulative distortion metric and a no-reference sparse prediction model for packet prioritization in encoded video transmission}},
url = {http://ieeexplore.ieee.org/document/7532728/},
volume = {2016-Augus},
year = {2016}
}

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The proposed metric, henceforth referred to as Cumulative Distortion using Structural Similarity (CDSSIM), quantifies the overall structural distortion resulting from bidirectional error propagation in predictively coded, motion compensated videos. Furthermore, we present a No-Reference (NR) sparse regression model to predict the proposed CDSSIM metric using pre-defined features associated with slice loss. The Least Absolute Shrinkage and Selection Operator (LASSO) method is applied for two resolution formats with features extracted solely from the encoded bit-stream. Standardized statistical performance measures show that the model can predict the cumulative distortion to a high degree of accuracy. 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