@incollection{Arun2016,
abstract = {The authors present two methods for examining video quality using the Structural Similarity (SSIM) index: Iterative Distortion Estimate (IDE) and Cumulative Distortion using SSIM (CDSSIM). In the first method, three types of slices are iteratively reconstructed frame-by-frame for three different combinations of packet loss and the resulting distortions are combined using their probabilities to give the total expected distortion. In the second method, a cumulative measure of the overall distortion is computed by summing the inter-frame propagation impact to all frames affected by a slice loss. Furthermore, the authors develop a No-Reference (NR) sparse regression framework for predicting the CDSSIM metric to circumvent the real-time computational complexity in streaming video applications. The two methods are evaluated in resource allocation and packet prioritization schemes and experimental results show improved performance and better end-user quality. The accuracy of the predicted CDSSIM values is studied using standard performance measures and a Quartile-Based Prioritization (QBP) scheme.},
author = {Sankisa, Arun and Pandremmenou, Katerina and Pahalawatta, Peshala V. and Kondi, Lisimachos P. and Katsaggelos, Aggelos K.},
booktitle = {Biometrics},
doi = {10.4018/978-1-5225-0983-7.ch028},
isbn = {9781522509844},
pages = {690--709},
publisher = {IGI Global},
title = {{SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels}},
url = {http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-0983-7.ch028},
volume = {7},
year = {2017}
}

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