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An add-on video compression codec based on content-adaptive sparse super-resolution reconstructions
Author(s): Shu Yang; Jianmin Jiang
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Paper Abstract

In this paper, we introduce an idea of content-adaptive sparse reconstruction to achieve optimized magnification quality for those down sampled video frames, to which two stages of pruning are applied to select the closest correlated images for construction of an over-complete dictionary and drive the sparse representation of its enlarged frame. In this way, not only the sampling and dictionary training process is accelerated and optimized in accordance with the input frame content, but also an add-on video compression codec can be further developed by applying such scheme as a preprocessor to any standard video compression algorithm. Our extensive experiments illustrate that (i) the proposed content-adaptive sparse reconstruction outperforms the existing benchmark in terms of super-resolution quality; (ii) When applied to H.264, one of the international video compression standards, the proposed add-on video codec can achieve three times more compression while maintaining competitive decoding quality.

Paper Details

Date Published: 8 February 2017
PDF: 7 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102251I (8 February 2017); doi: 10.1117/12.2266112
Show Author Affiliations
Shu Yang, Tianjin Univ. (China)
Jianmin Jiang, Shenzhen Univ. (China)


Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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