The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Data Partitioning: A Video Source-Coding Technique for Layered Video and Error Resilience
Abstract
Data partitioning is a source-coding technique that has existed in one form or another in the standardized hybrid video codecs up to recent times. In essence, it is a method of prioritizing coding data, resulting in video layers that can be separately communicated across an error-prone network. The Chapter includes the background that has led to data partitioning being included in the standardized codecs. As this Chapter discusses, it differs from scalable video because the output from conventional, single-layer encoders can be converted to multi-layer form, rather than requiring specialist codec extensions. It is shown that the methods of forming the partitions so far employed are: dividing transformed, residual coefficients into two or more layers; and dividing coded data by function into headers, intra-, and inter-coded residuals to form three or more layers. It is also shown how layering naturally combines with protection by channel coding. Used as an error resilience tool, data partitioning presents a low overhead method, suitable for benign as well as bad channels. And in the three-layer variety, error concealment at the decoder can significantly aid the reconstruction of damaged video frames. The Chapter will be of particular interest to developers charged with making a mobile, low-latency, or interactive video streaming application robust, as they can select from the data-partitioning methods and apply them to open-source code of the recent High Efficiency Video Coding (HEVC) codec standard. Broadcast TV can also benefit from data partitioning. Developers of codecs additionally will find in this Chapter a guide to research and ideas about data partitioning which could be incorporated into future codecs.
Related Content
|
Raquel Sánchez Ruiz, Isabel López Cirugeda.
© 2024.
22 pages.
|
|
Rocío Luque-González, Inmaculada Marín-López, Mercedes Gómez-López.
© 2024.
22 pages.
|
|
Bima Sapkota, Xuwei Luo, Muna Sapkota, Murat Akarsu, Emmanuel Deogratias, Daphne Fauber, Rose Mbewe, Fidelis Mumba, Ram Krishna Panthi, Jill Newton, JoAnn Phillion.
© 2024.
34 pages.
|
|
Karen Collett, Alina Slapac, Sarah A. Coppersmith, Jingxin Cheng.
© 2024.
29 pages.
|
|
Maria Ines Marino, Stephanie Tadal, Nurhayat Bilge.
© 2024.
25 pages.
|
|
Jaqueline Naidoo, Noah Borrero.
© 2024.
19 pages.
|
|
Crystal Machado, Tami Seifert.
© 2024.
20 pages.
|
|
|