Compressive video sampling

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the Discrete Cosine Transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for Magnetic Resonance Imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used.
Original languageEnglish
Title of host publicationProceedings of the 16th IEEE international conference on Image processing
Place of PublicationPiscataway, N.J.
PublisherIEEE
Pages3001-3004
Number of pages3
ISBN (Print)978-1-4244-5653-6
Publication statusPublished - 2009

Fingerprint

Sampling
Recovery
Discrete cosine transforms
Magnetic Resonance Imaging

Keywords

  • image processing
  • image reconstruction
  • video

Cite this

Stankovic, V., Stankovic, L., & Cheng, S. (2009). Compressive video sampling. In Proceedings of the 16th IEEE international conference on Image processing (pp. 3001-3004). Piscataway, N.J.: IEEE.
Stankovic, V. ; Stankovic, L. ; Cheng, S. / Compressive video sampling. Proceedings of the 16th IEEE international conference on Image processing. Piscataway, N.J. : IEEE, 2009. pp. 3001-3004
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title = "Compressive video sampling",
abstract = "Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the Discrete Cosine Transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for Magnetic Resonance Imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used.",
keywords = "image processing, image reconstruction, video",
author = "V. Stankovic and L. Stankovic and S. Cheng",
note = "{\circledC} 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.",
year = "2009",
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pages = "3001--3004",
booktitle = "Proceedings of the 16th IEEE international conference on Image processing",
publisher = "IEEE",

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Stankovic, V, Stankovic, L & Cheng, S 2009, Compressive video sampling. in Proceedings of the 16th IEEE international conference on Image processing. IEEE, Piscataway, N.J., pp. 3001-3004.

Compressive video sampling. / Stankovic, V.; Stankovic, L.; Cheng, S.

Proceedings of the 16th IEEE international conference on Image processing. Piscataway, N.J. : IEEE, 2009. p. 3001-3004.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Compressive video sampling

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N1 - © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

PY - 2009

Y1 - 2009

N2 - Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the Discrete Cosine Transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for Magnetic Resonance Imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used.

AB - Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the Discrete Cosine Transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for Magnetic Resonance Imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used.

KW - image processing

KW - image reconstruction

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Stankovic V, Stankovic L, Cheng S. Compressive video sampling. In Proceedings of the 16th IEEE international conference on Image processing. Piscataway, N.J.: IEEE. 2009. p. 3001-3004