Graph-cut-based compression algorithm for compressed-sensed image acquisition

Julide Gulen Alaydin; Seden Hazal Gulen; Maria Trocan; Behcet Ugur Toreyin

doi:10.1109/SIU.2014.6830726

Graph-cut-based compression algorithm for compressed-sensed image acquisition

Julide Gulen Alaydin, Seden Hazal Gulen, Maria Trocan, Behcet Ugur Toreyin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The purpose of the paper is to find the best quantizer allocation for compressed-sensed acquired images, by using a graph-cut quantizer allocation method. The compressed sensed acquisition is realized in a block-based manner, using a random projection matrix, and on the obtained block measurements a graph-cut-based quantizer allocation method is applied, in order to further reduce the bitrate associated to the measurements. Finally, the quantized measurements are reconstructed using a Smooth Projected Landweber recovery method. The proposed compression method for compressed sensed acquisition shows better results when compared to JPEG2000.

Original language	English
Title of host publication	2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
Publisher	IEEE Computer Society
Pages	2310-2313
Number of pages	4
ISBN (Print)	9781479948741
DOIs	https://doi.org/10.1109/SIU.2014.6830726
Publication status	Published - 2014
Externally published	Yes
Event	2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Trabzon, Turkey Duration: 23 Apr 2014 → 25 Apr 2014

Publication series

Name	2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

Conference

Conference	2014 22nd Signal Processing and Communications Applications Conference, SIU 2014
Country/Territory	Turkey
City	Trabzon
Period	23/04/14 → 25/04/14

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10.1109/SIU.2014.6830726

Cite this

Alaydin, J. G., Gulen, S. H., Trocan, M., & Toreyin, B. U. (2014). Graph-cut-based compression algorithm for compressed-sensed image acquisition. In 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings (pp. 2310-2313). Article 6830726 (2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings). IEEE Computer Society. https://doi.org/10.1109/SIU.2014.6830726

Alaydin, Julide Gulen ; Gulen, Seden Hazal ; Trocan, Maria et al. / Graph-cut-based compression algorithm for compressed-sensed image acquisition. 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings. IEEE Computer Society, 2014. pp. 2310-2313 (2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings).

@inproceedings{9901447a8da04ca88fab910ce2c350bf,

title = "Graph-cut-based compression algorithm for compressed-sensed image acquisition",

abstract = "The purpose of the paper is to find the best quantizer allocation for compressed-sensed acquired images, by using a graph-cut quantizer allocation method. The compressed sensed acquisition is realized in a block-based manner, using a random projection matrix, and on the obtained block measurements a graph-cut-based quantizer allocation method is applied, in order to further reduce the bitrate associated to the measurements. Finally, the quantized measurements are reconstructed using a Smooth Projected Landweber recovery method. The proposed compression method for compressed sensed acquisition shows better results when compared to JPEG2000.",

author = "Alaydin, {Julide Gulen} and Gulen, {Seden Hazal} and Maria Trocan and Toreyin, {Behcet Ugur}",

year = "2014",

doi = "10.1109/SIU.2014.6830726",

language = "English",

isbn = "9781479948741",

series = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",

publisher = "IEEE Computer Society",

pages = "2310--2313",

booktitle = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",

address = "United States",

note = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 ; Conference date: 23-04-2014 Through 25-04-2014",

}

Alaydin, JG, Gulen, SH, Trocan, M & Toreyin, BU 2014, Graph-cut-based compression algorithm for compressed-sensed image acquisition. in 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings., 6830726, 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings, IEEE Computer Society, pp. 2310-2313, 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014, Trabzon, Turkey, 23/04/14. https://doi.org/10.1109/SIU.2014.6830726

Graph-cut-based compression algorithm for compressed-sensed image acquisition. / Alaydin, Julide Gulen; Gulen, Seden Hazal; Trocan, Maria et al.
2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings. IEEE Computer Society, 2014. p. 2310-2313 6830726 (2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Graph-cut-based compression algorithm for compressed-sensed image acquisition

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AU - Gulen, Seden Hazal

AU - Trocan, Maria

AU - Toreyin, Behcet Ugur

PY - 2014

Y1 - 2014

N2 - The purpose of the paper is to find the best quantizer allocation for compressed-sensed acquired images, by using a graph-cut quantizer allocation method. The compressed sensed acquisition is realized in a block-based manner, using a random projection matrix, and on the obtained block measurements a graph-cut-based quantizer allocation method is applied, in order to further reduce the bitrate associated to the measurements. Finally, the quantized measurements are reconstructed using a Smooth Projected Landweber recovery method. The proposed compression method for compressed sensed acquisition shows better results when compared to JPEG2000.

AB - The purpose of the paper is to find the best quantizer allocation for compressed-sensed acquired images, by using a graph-cut quantizer allocation method. The compressed sensed acquisition is realized in a block-based manner, using a random projection matrix, and on the obtained block measurements a graph-cut-based quantizer allocation method is applied, in order to further reduce the bitrate associated to the measurements. Finally, the quantized measurements are reconstructed using a Smooth Projected Landweber recovery method. The proposed compression method for compressed sensed acquisition shows better results when compared to JPEG2000.

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U2 - 10.1109/SIU.2014.6830726

DO - 10.1109/SIU.2014.6830726

M3 - Conference contribution

AN - SCOPUS:84903788902

SN - 9781479948741

T3 - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

SP - 2310

EP - 2313

BT - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

PB - IEEE Computer Society

T2 - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014

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ER -

Alaydin JG, Gulen SH, Trocan M, Toreyin BU. Graph-cut-based compression algorithm for compressed-sensed image acquisition. In 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings. IEEE Computer Society. 2014. p. 2310-2313. 6830726. (2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings). doi: 10.1109/SIU.2014.6830726

Graph-cut-based compression algorithm for compressed-sensed image acquisition

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