Detail publikace

Spectrum-based approach to classifying video sequences for encoding experiments

Originální název

Spectrum-based approach to classifying video sequences for encoding experiments

Anglický název

Spectrum-based approach to classifying video sequences for encoding experiments

Jazyk

en

Originální abstrakt

With the evolution of video compression algorithms, transmission techniques as well as error concealment tools, benchmarking is essential to assess the performance of emerging techniques. This paper discusses the selection of test material in order to obtain a representative set of different scene character- istics. While the most common approach is relying on the Spatial Information and Temporal Information parameters, our results show that the spatio-temporal spectrum information can be used to classify sequences with high reliability while maintaining low computational complexity.

Anglický abstrakt

With the evolution of video compression algorithms, transmission techniques as well as error concealment tools, benchmarking is essential to assess the performance of emerging techniques. This paper discusses the selection of test material in order to obtain a representative set of different scene character- istics. While the most common approach is relying on the Spatial Information and Temporal Information parameters, our results show that the spatio-temporal spectrum information can be used to classify sequences with high reliability while maintaining low computational complexity.

BibTex


@inproceedings{BUT148671,
  author="Martin {Slanina}",
  title="Spectrum-based approach to classifying video sequences for encoding experiments",
  annote="With the evolution of video compression algorithms, transmission techniques as well as error concealment tools, benchmarking is essential to assess the performance of emerging techniques. This paper discusses the selection of test material in order to obtain a representative set of different scene character- istics. While the most common approach is relying on the Spatial Information and Temporal Information parameters, our results show that the spatio-temporal spectrum information can be used to classify sequences with high reliability while maintaining low computational complexity.",
  booktitle="2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP)",
  chapter="148671",
  doi="10.1109/IWSSIP.2018.8439344",
  howpublished="electronic, physical medium",
  year="2018",
  month="june",
  pages="1--5",
  type="conference paper"
}