Complexity modeling for coarse grain scalable (CGS) video decoding

Chun Yen Yu; Wei Hsiang Chiu; Chih Hung Kuo

doi:10.1109/ICCCAS.2013.6765182

Complexity modeling for coarse grain scalable (CGS) video decoding

Chun Yen Yu, Wei Hsiang Chiu, Chih Hung Kuo

Department of Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper proposes a hybrid model to predict CGS-SVC decoding complexity. We take advantage of both the statistic characteristic of complexity features and linear relationship between quality layers to model the complexity. Experimental results show that the proposed method provides a good prediction accuracy for all quality layer. The whole average prediction error of test sequences is 1.51% approximately. Furthermore, the target platform can decode the suitable quality layer by our layer decision mechanism and an accurate prediction result.

Original language	English
Pages	42-45
Number of pages	4
DOIs	https://doi.org/10.1109/ICCCAS.2013.6765182
Publication status	Published - 2013
Event	2013 International Conference on Communications, Circuits and Systems, ICCCAS 2013 - Chengdu, China Duration: 2013 Nov 15 → 2013 Nov 17

Other

Other	2013 International Conference on Communications, Circuits and Systems, ICCCAS 2013
Country/Territory	China
City	Chengdu
Period	13-11-15 → 13-11-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture

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10.1109/ICCCAS.2013.6765182

Cite this

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title = "Complexity modeling for coarse grain scalable (CGS) video decoding",

abstract = "This paper proposes a hybrid model to predict CGS-SVC decoding complexity. We take advantage of both the statistic characteristic of complexity features and linear relationship between quality layers to model the complexity. Experimental results show that the proposed method provides a good prediction accuracy for all quality layer. The whole average prediction error of test sequences is 1.51% approximately. Furthermore, the target platform can decode the suitable quality layer by our layer decision mechanism and an accurate prediction result.",

author = "Yu, {Chun Yen} and Chiu, {Wei Hsiang} and Kuo, {Chih Hung}",

year = "2013",

doi = "10.1109/ICCCAS.2013.6765182",

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note = "2013 International Conference on Communications, Circuits and Systems, ICCCAS 2013 ; Conference date: 15-11-2013 Through 17-11-2013",

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AU - Yu, Chun Yen

AU - Chiu, Wei Hsiang

AU - Kuo, Chih Hung

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AB - This paper proposes a hybrid model to predict CGS-SVC decoding complexity. We take advantage of both the statistic characteristic of complexity features and linear relationship between quality layers to model the complexity. Experimental results show that the proposed method provides a good prediction accuracy for all quality layer. The whole average prediction error of test sequences is 1.51% approximately. Furthermore, the target platform can decode the suitable quality layer by our layer decision mechanism and an accurate prediction result.

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T2 - 2013 International Conference on Communications, Circuits and Systems, ICCCAS 2013

Y2 - 15 November 2013 through 17 November 2013

ER -

Complexity modeling for coarse grain scalable (CGS) video decoding

Abstract

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