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Multiple Adaptations and Content-Adaptive FEC Using Parameterized RD Model for Embedded Wavelet Video

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Abstract

Scalable video coding (SVC) has been an active research topic for the past decade. In the past, most SVC technologies were based on a coarse-granularity scalable model which puts many scalability constraints on the encoded bitstreams. As a result, the application scenario of adapting a preencoded bitstream multiple times along the distribution chain has not been seriously investigated before. In this paper, a model-based multiple-adaptation framework based on a wavelet video codec, MC-EZBC, is proposed. The proposed technology allows multiple adaptations on both the video data and the content-adaptive FEC protection codes. For multiple adaptations of video data, rate-distortion information must be embedded within the video bitstream in order to allow rate-distortion optimized operations for each adaptation. Experimental results show that the proposed method reduces the amount of side information by more than 50% on average when compared to the existing technique. It also reduces the number of iterations required to perform the tier-2 entropy coding by more than 64% on average. In addition, due to the nondiscrete nature of the rate-distortion model, the proposed framework also enables multiple adaptations of content-adaptive FEC protection scheme for more flexible error-resilient transmission of bitstreams.

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Correspondence to Ya-Huei Yu.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Yu, Y., Ho, C. & Tsai, C. Multiple Adaptations and Content-Adaptive FEC Using Parameterized RD Model for Embedded Wavelet Video. EURASIP J. Adv. Signal Process. 2007, 070914 (2007) doi:10.1155/2007/70914

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Keywords

  • Entropy
  • Research Topic
  • Multiple Time
  • Quantum Information
  • Scalable Model