Hotrakool, W. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2014) Running Gaussian reference-based reconstruction for video compressed sensing. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 04-09 May 2014, Florence, Italy. IEEE , pp. 2001-2005. ISBN 9781479928934
Abstract
Our recent work has shown that quality of compressed sensing reconstruction can be improved immensely by minimising the error between the signal and a correlated reference, as opposed to the conventional l 1 -minimisation of the data measurements. This paper introduces a method for online estimating suitable references for video sequences using the running Gaussian average. The proposed method can provide robustness to video content changes as well as reconstruction noise. The experimental results demonstrate the performance of this method to be superior to those of the state-of-the-art l 1 -min methods. The results are comparable to the lossless reference reconstruction approach.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Image reconstruction; Compressed sensing; PSNR; Stability criteria; Visualization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Oct 2021 12:24 |
Last Modified: | 02 Nov 2021 09:44 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/icassp.2014.6853949 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179775 |