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Self-Adaptive Video Encoder: Comparison of Multiple Adaptation Strategies Made Simple (Artifact)

Authors Martina Maggio, Alessandro Vittorio Papadopoulos, Antonio Filieri, Henry Hoffmann



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DARTS.3.1.2.pdf
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Author Details

Martina Maggio
Alessandro Vittorio Papadopoulos
Antonio Filieri
Henry Hoffmann

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Martina Maggio, Alessandro Vittorio Papadopoulos, Antonio Filieri, and Henry Hoffmann. Self-Adaptive Video Encoder: Comparison of Multiple Adaptation Strategies Made Simple (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 2:1-2:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/DARTS.3.1.2

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Abstract

This paper presents an adaptive video encoder that can be used to compare the behavior of different adaptation strategies using multiple actuators to steer the encoder towards a global goal, composed of multiple conflicting objectives. A video camera produces frames that the encoder manipulates with the objective of matching some space requirement to fit a given communication channel. A second objective is to maintain a given similarity index between the manipulated frames and the original ones. To achieve the goal, the software can change three parameters: the quality of the encoding, the noise reduction filter radius and the sharpening filter radius. In most cases the objectives - small encoded size and high quality - conflict, since a larger frame would have a higher similarity index to its original counterpart. This makes the problem difficult from the control perspective and makes the case study appealing to compare different adaptation strategies.
Keywords
  • self-adaptive software
  • video encoding
  • comparison
  • control theory

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References

  1. Antonio Filieri, Henry Hoffmann, and Martina Maggio. Automated design of self-adaptive software with control-theoretical formal guarantees. In Proceedings of the 36th International Conference on Software Engineering, ICSE, pages 299-310, New York, NY, USA, 2014. ACM. URL: http://doi.acm.org/10.1145/2568225.2568272, URL: http://dx.doi.org/10.1145/2568225.2568272.
  2. Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. Google Scholar
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