Subaperture image segmentation for lossless compression

Ionut Schiopu; Moncef Gabbouj; Alexandres Iosifidis; Bing Zeng; Shuaicheng Liu

doi:10.1109/IPTA.2017.8310083

Subaperture image segmentation for lossless compression

Ionut Schiopu
, Moncef Gabbouj
, Alexandres Iosifidis
, Bing Zeng
, Shuaicheng Liu

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

4 Citations (Scopus)

Abstract

The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images.

Original language	English
Title of host publication	Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538618417
DOIs	https://doi.org/10.1109/IPTA.2017.8310083
Publication status	Published - 8 Mar 2018
Publication type	A4 Article in conference proceedings
Event	International Conference on Image Processing Theory, Tools and Applications - Montreal, Canada Duration: 28 Nov 2017 → 1 Dec 2017

Conference

Conference	International Conference on Image Processing Theory, Tools and Applications
Country/Territory	Canada
City	Montreal
Period	28/11/17 → 1/12/17

Keywords

image segmentation
Lossless compression
plenoptic image
quantum cut segmentation

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Signal Processing
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/IPTA.2017.8310083

Cite this

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title = "Subaperture image segmentation for lossless compression",

abstract = "The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2\% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images.",

keywords = "image segmentation, Lossless compression, plenoptic image, quantum cut segmentation",

author = "Ionut Schiopu and Moncef Gabbouj and Alexandres Iosifidis and Bing Zeng and Shuaicheng Liu",

year = "2018",

month = mar,

day = "8",

doi = "10.1109/IPTA.2017.8310083",

language = "English",

pages = "1--6",

booktitle = "Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017",

publisher = "IEEE",

address = "United States",

note = "International Conference on Image Processing Theory, Tools and Applications ; Conference date: 28-11-2017 Through 01-12-2017",

}

Schiopu, I, Gabbouj, M , Iosifidis, A, Zeng, B & Liu, S 2018, Subaperture image segmentation for lossless compression. in Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017. IEEE, pp. 1-6, International Conference on Image Processing Theory, Tools and Applications, Montreal, Canada, 28/11/17. https://doi.org/10.1109/IPTA.2017.8310083

Subaperture image segmentation for lossless compression. / Schiopu, Ionut; Gabbouj, Moncef ; Iosifidis, Alexandres et al.
Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017. IEEE, 2018. p. 1-6.

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

TY - GEN

T1 - Subaperture image segmentation for lossless compression

AU - Schiopu, Ionut

AU - Gabbouj, Moncef

AU - Iosifidis, Alexandres

AU - Zeng, Bing

AU - Liu, Shuaicheng

PY - 2018/3/8

Y1 - 2018/3/8

N2 - The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images.

AB - The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images.

KW - image segmentation

KW - Lossless compression

KW - plenoptic image

KW - quantum cut segmentation

U2 - 10.1109/IPTA.2017.8310083

DO - 10.1109/IPTA.2017.8310083

M3 - Conference contribution

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BT - Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017

PB - IEEE

T2 - International Conference on Image Processing Theory, Tools and Applications

Y2 - 28 November 2017 through 1 December 2017

ER -

Subaperture image segmentation for lossless compression

Abstract

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

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Cite this