NDLI: Video dimension reduction and coding using Multiple Tensor Rank-R Decomposition

Content Provider	IEEE Xplore Digital Library
Author	Bingyin Zhou Fan Zhang Lizhong Peng
Copyright Year	2011
Description	Author affiliation: School of Mathematics and Information Sciences, Hebei Normal University, 050016, China (Bingyin Zhou) \|\| School of Mathematical Sciences, Peking University, Beijing 100871, China (Fan Zhang; Lizhong Peng)
Abstract	In this paper, we propose a dimension reduction algorithm for video coding based on tensor decomposition. Our Multiple Tensor Rank-R Decomposition (MTRD) algorithm uses rank-R tensors to repeatedly approximate the high dimensional video data, so that the dimension of the data can be greatly reduced. Through quantifying and coding these tensor decomposition coefficients, we can achieve a better video quality with the same compression ratio as H.264/AVC. In particular, we find that our method can implement a high-quality coding at a very low bit-rate for a class of videos with regular texture.
Starting Page	330
Ending Page	334
File Size	855252
Page Count	5
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6099928
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress PSNR low bit-rate Heuristic algorithms Approximation algorithms Educational institutions Encoding Vectors video coding dimension reduction computational complexity tensor decomposition
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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