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http://hdl.handle.net/1893/32104| Appears in Collections: | Computing Science and Mathematics Conference Papers and Proceedings |
| Author(s): | Bhowmik, Deepayan Abhayaratne, Charith Green, Stuart |
| Contact Email: | deepayan.bhowmik@stir.ac.uk |
| Title: | Video Watermarking for Persistent and Robust Tracking of Entertainment Content (PARTEC) |
| Editor(s): | Mandal, Jyotsna Kumar Mukherjee, Imon Bakshi, Sambit Chatterji, Sanjay Sa, Pankaj K |
| Citation: | Bhowmik D, Abhayaratne C & Green S (2021) Video Watermarking for Persistent and Robust Tracking of Entertainment Content (PARTEC). In: Mandal JK, Mukherjee I, Bakshi S, Chatterji S & Sa PK (eds.) Computational Intelligence and Machine Learning. Advances in Intelligent Systems and Computing, 1276. 7th International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2019), West Bengal, India. Singapore: Springer, pp. 185-198. https://doi.org/10.1007/978-981-15-8610-1_19 |
| Issue Date: | 2021 |
| Date Deposited: | 29-Nov-2020 |
| Series/Report no.: | Advances in Intelligent Systems and Computing, 1276 |
| Conference Name: | 7th International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2019) |
| Conference Location: | West Bengal, India |
| Abstract: | The exploitation of film and video content on physical media, broadcast and Internet involves working with many large media files. The move to file-based workflows necessitates the copying and transfer of digital assets amongst many parties, but the detachment of assets and their metadata leads to issues of reliability, quality and security. This paper proposes a novel watermarking-based approach to deliver a unique solution to enable digital media assets to be maintained with their metadata persistently and robustly. Watermarking-based solution for entertainment content manifests new challenges, including maintaining high quality of the media content, robustness to compression and file format changes and synchronisation against scene editing. The proposed work addresses these challenges and demonstrates interoperability with an existing industrial software framework for media asset management (MAM) systems. |
| Status: | AM - Accepted Manuscript |
| Rights: | This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is a post-peer-review, pre-copyedit version of a paper published in Mandal JK, Mukherjee I, Bakshi S, Chatterji S & Sa PK (eds.) Computational Intelligence and Machine Learning. Advances in Intelligent Systems and Computing, 1276. 7th International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2019), West Bengal, India. Singapore: Springer, pp. 185-198. The final authenticated version is available online at: https://doi.org/10.1007/978-981-15-8610-1_19 |
| Licence URL(s): | https://storre.stir.ac.uk/STORREEndUserLicence.pdf |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| PARTEC_icacni2019.pdf | Fulltext - Accepted Version | 663.25 kB | Adobe PDF | View/Open |
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