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Nowadays, networks and terminals with diverse characteristics of bandwidth and capabilities coexist. To ensure a good quality of experience, this diverse environment demands adaptability of the video stream. In general, video contents are compressed to save storage capacity and to reduce the bandwidth required for its transmission. Therefore, if these compressed video streams were compressed using scalable video coding schemes, they would be able to adapt to those heterogeneous networks and a wide range of terminals. Since the majority of the multimedia contents are compressed using H.264/AVC, they cannot benefit from that scalability. This paper proposes a technique to convert an H.264/AVC bitstream without scalability to a scalable bitstream with temporal scalability in Main Profile by accelerating the mode decision task of the SVC encoding stage using Machine Learning tools. The results show that when our technique is applied, the complexity is reduced by 87% while maintaining coding efficiency.
Keywords
Temporal Scalability, Machine Learning, Transcoding, H.264/AVC, Scalable Video Coding (SVC), STANDARD

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MLA
Garrido-Cantos, Rosario, Jan De Cock, José Luis Martinez, et al. “H.264/AVC-to-SVC Temporal Transcoding Using Machine Learning.” Frontiers in Artificial Intelligence and Applications. Ed. M Grana et al. Vol. 243. Amsterdam, The Netherlands: IOS PRESS, 2012. 1693–1702. Print.
APA
Garrido-Cantos, Rosario, De Cock, J., Martinez, J. L., Van Leuven, S., Cuenca, P., & Garrido, A. (2012). H.264/AVC-to-SVC temporal transcoding using machine learning. In M. Grana, C. Toro, J. Posada, R. Howlett, & L. Jain (Eds.), Frontiers in Artificial Intelligence and Applications (Vol. 243, pp. 1693–1702). Presented at the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Amsterdam, The Netherlands: IOS PRESS.
Chicago author-date
Garrido-Cantos, Rosario, Jan De Cock, José Luis Martinez, Sebastiaan Van Leuven, Pedro Cuenca, and Antonio Garrido. 2012. “H.264/AVC-to-SVC Temporal Transcoding Using Machine Learning.” In Frontiers in Artificial Intelligence and Applications, ed. M Grana, C Toro, J Posada, RJ Howlett, and LC Jain, 243:1693–1702. Amsterdam, The Netherlands: IOS PRESS.
Chicago author-date (all authors)
Garrido-Cantos, Rosario, Jan De Cock, José Luis Martinez, Sebastiaan Van Leuven, Pedro Cuenca, and Antonio Garrido. 2012. “H.264/AVC-to-SVC Temporal Transcoding Using Machine Learning.” In Frontiers in Artificial Intelligence and Applications, ed. M Grana, C Toro, J Posada, RJ Howlett, and LC Jain, 243:1693–1702. Amsterdam, The Netherlands: IOS PRESS.
Vancouver
1.
Garrido-Cantos R, De Cock J, Martinez JL, Van Leuven S, Cuenca P, Garrido A. H.264/AVC-to-SVC temporal transcoding using machine learning. In: Grana M, Toro C, Posada J, Howlett R, Jain L, editors. Frontiers in Artificial Intelligence and Applications. Amsterdam, The Netherlands: IOS PRESS; 2012. p. 1693–702.
IEEE
[1]
R. Garrido-Cantos, J. De Cock, J. L. Martinez, S. Van Leuven, P. Cuenca, and A. Garrido, “H.264/AVC-to-SVC temporal transcoding using machine learning,” in Frontiers in Artificial Intelligence and Applications, San Sebastian, SPAIN, 2012, vol. 243, pp. 1693–1702.
@inproceedings{5821605,
  abstract     = {Nowadays, networks and terminals with diverse characteristics of bandwidth and capabilities coexist. To ensure a good quality of experience, this diverse environment demands adaptability of the video stream. In general, video contents are compressed to save storage capacity and to reduce the bandwidth required for its transmission. Therefore, if these compressed video streams were compressed using scalable video coding schemes, they would be able to adapt to those heterogeneous networks and a wide range of terminals. Since the majority of the multimedia contents are compressed using H.264/AVC, they cannot benefit from that scalability. This paper proposes a technique to convert an H.264/AVC bitstream without scalability to a scalable bitstream with temporal scalability in Main Profile by accelerating the mode decision task of the SVC encoding stage using Machine Learning tools. The results show that when our technique is applied, the complexity is reduced by 87% while maintaining coding efficiency.},
  author       = {Garrido-Cantos, Rosario and De Cock, Jan and Martinez, José Luis and Van Leuven, Sebastiaan and Cuenca, Pedro and Garrido, Antonio},
  booktitle    = {Frontiers in Artificial Intelligence and Applications},
  editor       = {Grana, M and Toro, C and Posada, J and Howlett, RJ and Jain, LC },
  isbn         = {9781614991052},
  issn         = {0922-6389},
  keywords     = {Temporal Scalability,Machine Learning,Transcoding,H.264/AVC,Scalable Video Coding (SVC),STANDARD},
  language     = {eng},
  location     = {San Sebastian, SPAIN},
  pages        = {1693--1702},
  publisher    = {IOS PRESS},
  title        = {H.264/AVC-to-SVC temporal transcoding using machine learning},
  url          = {http://dx.doi.org/10.3233/978-1-61499-105-2-1693},
  volume       = {243},
  year         = {2012},
}

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