SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels

Arun Sankisa, Katerina Pandremmenou, Peshala V. Pahalawatta, Lisimachos P. Kondi and Aggelos K. Katsaggelos
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Arun Sankisa: Northwestern University, Evanston, IL, USA
Katerina Pandremmenou: University of Ioannina, Ioannina, Greece
Peshala V. Pahalawatta: AT&T, Inc., El Segundo, CA, USA
Lisimachos P. Kondi: University of Ioannina, Ioannina, Greece
Aggelos K. Katsaggelos: Northwestern University, Evanston, IL, USA

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2016, vol. 7, issue 3, 34-52

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.

Date: 2016
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