SSIM-based distortion estimation for optimized video transmission over inherently noisy channels

Arun Sankisa, Peshala V. Pahalawatta*, Katerina Pandremmenou, Lisimachos P. Kondi, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationBiometrics
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Pages690-709
Number of pages20
ISBN (Electronic)9781522509844
ISBN (Print)9781522509837
DOIs
StatePublished - Aug 30 2016

ASJC Scopus subject areas

  • Computer Science(all)

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