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SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels
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Author(s): Arun Sankisa (Northwestern University, USA), Katerina Pandremmenou (University of Ioannina, Greece), Peshala V. Pahalawatta (AT&T, Inc., USA), Lisimachos P. Kondi (University of Ioannina, Greece)and Aggelos K. Katsaggelos (Northwestern University, USA)
Copyright: 2017
Pages: 20
Source title:
Biometrics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0983-7.ch028
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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.
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