Adaptive embedding and detection for improved video watermarking

Isao Echizen, Yasuhiro Fujii, Takaaki Yamada, Satoru Tezuka, Hiroshi Yoshiura

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Video watermarks must maintain picture quality and withstand video processing. Resolving the tradeoff between these conflicting requirements has been one of the central problems of video watermarking. Previous video watermarking methods, however, have trouble satisfying these conflicting quality and survivability requirements because they utilize the watermarking methods for still picture and neglect the properties of motion pictures. To resolve this tradeoff, we describe adaptive video embedding and detection techniques that utilize the properties of motion pictures. Motion-adaptive embedding can allocate watermarks to picture areas which are adaptively based on motion properties of video and, statistically adaptive detection can control the accumulation of watermarks based on the statistical properties of video to prevent degradation in the watermark signal by video processing. Experimental evaluations using actual motion pictures have shown that these techniques can be effective for resolving the tradeoff and can be widely used in the pixel-based watermarking.

Original languageEnglish
Title of host publicationIntelligent Multimedia Data Hiding
Subtitle of host publicationNew Directions
EditorsJeng-Shyang Pan, Hsiang-Cheh Huang, Lakhmi Jain, Wai-Chai Fang
Pages157-187
Number of pages31
DOIs
Publication statusPublished - 2007 May 8
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume58
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Echizen, I., Fujii, Y., Yamada, T., Tezuka, S., & Yoshiura, H. (2007). Adaptive embedding and detection for improved video watermarking. In J-S. Pan, H-C. Huang, L. Jain, & W-C. Fang (Eds.), Intelligent Multimedia Data Hiding: New Directions (pp. 157-187). (Studies in Computational Intelligence; Vol. 58). https://doi.org/10.1007/978-3-540-71169-8_7