Quality optimization of H.264/AVC video transmission over noisy environments using a sparse regression framework

K. Pandremmenou; N. Tziortziotis; S. Paluri; Weiyu Q. Zhang; K. Blekas; L. P. Kondi; S. Kumar

doi:10.1117/12.2077243

4 March 2015 Quality optimization of H.264/AVC video transmission over noisy environments using a sparse regression framework

K. Pandremmenou, N. Tziortziotis, S. Paluri, Weiyu Q. Zhang, K. Blekas, L. P. Kondi, S. Kumar

Author Affiliations +

Proceedings Volume 9410, Visual Information Processing and Communication VI; 94100D (2015) https://doi.org/10.1117/12.2077243
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

We propose the use of the Least Absolute Shrinkage and Selection Operator (LASSO) regression method in order to predict the Cumulative Mean Squared Error (CMSE), incurred by the loss of individual slices in video transmission. We extract a number of quality-relevant features from the H.264/AVC video sequences, which are given as input to the LASSO. This method has the benefit of not only keeping a subset of the features that have the strongest effects towards video quality, but also produces accurate CMSE predictions. Particularly, we study the LASSO regression through two different architectures; the Global LASSO (G.LASSO) and Local LASSO (L.LASSO). In G.LASSO, a single regression model is trained for all slice types together, while in L.LASSO, motivated by the fact that the values for some features are closely dependent on the considered slice type, each slice type has its own regression model, in an e ort to improve LASSO's prediction capability. Based on the predicted CMSE values, we group the video slices into four priority classes. Additionally, we consider a video transmission scenario over a noisy channel, where Unequal Error Protection (UEP) is applied to all prioritized slices. The provided results demonstrate the efficiency of LASSO in estimating CMSE with high accuracy, using only a few features. les that typically contain high-entropy data, producing a footprint that is far less conspicuous than existing methods. The system uses a local web server to provide a le system, user interface and applications through an web architecture.

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K. Pandremmenou, N. Tziortziotis, S. Paluri, Weiyu Q. Zhang, K. Blekas, L. P. Kondi, and S. Kumar "Quality optimization of H.264/AVC video transmission over noisy environments using a sparse regression framework", Proc. SPIE 9410, Visual Information Processing and Communication VI, 94100D (4 March 2015); https://doi.org/10.1117/12.2077243

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