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

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

Arun Sankisa, Katerina Pandremmenou, Peshala V. Pahalawatta, Lisimachos P. Kondi, Aggelos K. Katsaggelos
Copyright: © 2017 |Pages: 20
ISBN13: 9781522509837|ISBN10: 1522509836|EISBN13: 9781522509844
DOI: 10.4018/978-1-5225-0983-7.ch028
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MLA

Sankisa, Arun, et al. "SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels." Biometrics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 690-709. https://doi.org/10.4018/978-1-5225-0983-7.ch028

APA

Sankisa, A., Pandremmenou, K., Pahalawatta, P. V., Kondi, L. P., & Katsaggelos, A. K. (2017). SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels. In I. Management Association (Ed.), Biometrics: Concepts, Methodologies, Tools, and Applications (pp. 690-709). IGI Global. https://doi.org/10.4018/978-1-5225-0983-7.ch028

Chicago

Sankisa, Arun, et al. "SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels." In Biometrics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 690-709. Hershey, PA: IGI Global, 2017. https://doi.org/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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