Lossless image coding using binary tree decomposition of prediction residuals

Mortuza Ali; Manzur Murshed; Shampa Shahriyar; Manoranjan Paul

doi:10.1109/PCS.2015.7170074

Lossless image coding using binary tree decomposition of prediction residuals

Mortuza Ali, Manzur Murshed, Shampa Shahriyar, Manoranjan Paul

Research output: Book chapter/Published conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

State-of-the-art lossless image compression schemes,such as, JPEG-LS and CALIC, have been proposed in the context adaptive predictive coding framework. These schemes involve a prediction step followed by context adaptive entropy coding of the residuals. It can be observed that there exist significant spatial correlation among the residuals after prediction. The efficient schemes proposed in the literature rely on context adaptive entropy coding to exploit this spatial correlation. In this paper, we propose an alternative approach to exploit this spatial correlation.The proposed scheme also involves a prediction stage. However,we resort to a binary tree based hierarchical decomposition technique to efficiently exploit the spatial correlation. On a set of standard test images, the proposed scheme, using the same predictor as JPEG-LS, achieved an overall compression gain of 2:1% against JPEG-LS.

Original language	English
Title of host publication	PCS 2015 Proceedings
Subtitle of host publication	2015 Picture Coding Symposium (PCS) with 2015 Packet Video Workshop (PV)
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	194-198
Number of pages	5
ISBN (Electronic)	9781479977833
DOIs	https://doi.org/10.1109/PCS.2015.7170074
Publication status	Published - 2015
Event	PCS 2015: 31st Picture Coding Symposium - Hotel Pullman Reef Hotel Casino, Cairns, Australia Duration: 31 May 2015 → 03 Jun 2015 http://www.pcs2015.org/

Conference

Conference	PCS 2015
Country/Territory	Australia
City	Cairns
Period	31/05/15 → 03/06/15
Other	The Picture Coding Symposium (PCS) is an international forum devoted specifically to advances in visual data coding. PCS is the pioneer conference, and has the longest history, in this field. Since 1969, PCS has provided the most exciting forum for the visual coding community from academia and industry. The 31st PCS will be held in Cairns, the heart of tropical North Queensland and gateway to the Great Barrier Reef. In line with the pioneering tradition of PCS, path breaking, challenging exploratory contributions are very welcome.
Internet address	http://www.pcs2015.org/

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10.1109/PCS.2015.7170074

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title = "Lossless image coding using binary tree decomposition of prediction residuals",

abstract = "State-of-the-art lossless image compression schemes,such as, JPEG-LS and CALIC, have been proposed in the context adaptive predictive coding framework. These schemes involve a prediction step followed by context adaptive entropy coding of the residuals. It can be observed that there exist significant spatial correlation among the residuals after prediction. The efficient schemes proposed in the literature rely on context adaptive entropy coding to exploit this spatial correlation. In this paper, we propose an alternative approach to exploit this spatial correlation.The proposed scheme also involves a prediction stage. However,we resort to a binary tree based hierarchical decomposition technique to efficiently exploit the spatial correlation. On a set of standard test images, the proposed scheme, using the same predictor as JPEG-LS, achieved an overall compression gain of 2:1% against JPEG-LS.",

keywords = "Image coding",

author = "Mortuza Ali and Manzur Murshed and Shampa Shahriyar and Manoranjan Paul",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2015. Grant ID (550a) = DP130103670. Event dates (773o) = 31 May - 3 June 2015; Parent title (773t) = IEEE Picture Coding Symposium.; PCS 2015 : 31st Picture Coding Symposium ; Conference date: 31-05-2015 Through 03-06-2015",

year = "2015",

doi = "10.1109/PCS.2015.7170074",

language = "English",

pages = "194--198",

booktitle = "PCS 2015 Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

url = "http://www.pcs2015.org/",

}

Ali, M, Murshed, M, Shahriyar, S & Paul, M 2015, Lossless image coding using binary tree decomposition of prediction residuals. in PCS 2015 Proceedings: 2015 Picture Coding Symposium (PCS) with 2015 Packet Video Workshop (PV). IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 194-198, PCS 2015, Cairns, Queensland, Australia, 31/05/15. https://doi.org/10.1109/PCS.2015.7170074

Lossless image coding using binary tree decomposition of prediction residuals. / Ali, Mortuza; Murshed, Manzur; Shahriyar, Shampa et al.

PCS 2015 Proceedings: 2015 Picture Coding Symposium (PCS) with 2015 Packet Video Workshop (PV). USA : IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 194-198.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Lossless image coding using binary tree decomposition of prediction residuals

AU - Ali, Mortuza

AU - Murshed, Manzur

AU - Shahriyar, Shampa

AU - Paul, Manoranjan

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2015. Grant ID (550a) = DP130103670. Event dates (773o) = 31 May - 3 June 2015; Parent title (773t) = IEEE Picture Coding Symposium.

PY - 2015

Y1 - 2015

N2 - State-of-the-art lossless image compression schemes,such as, JPEG-LS and CALIC, have been proposed in the context adaptive predictive coding framework. These schemes involve a prediction step followed by context adaptive entropy coding of the residuals. It can be observed that there exist significant spatial correlation among the residuals after prediction. The efficient schemes proposed in the literature rely on context adaptive entropy coding to exploit this spatial correlation. In this paper, we propose an alternative approach to exploit this spatial correlation.The proposed scheme also involves a prediction stage. However,we resort to a binary tree based hierarchical decomposition technique to efficiently exploit the spatial correlation. On a set of standard test images, the proposed scheme, using the same predictor as JPEG-LS, achieved an overall compression gain of 2:1% against JPEG-LS.

AB - State-of-the-art lossless image compression schemes,such as, JPEG-LS and CALIC, have been proposed in the context adaptive predictive coding framework. These schemes involve a prediction step followed by context adaptive entropy coding of the residuals. It can be observed that there exist significant spatial correlation among the residuals after prediction. The efficient schemes proposed in the literature rely on context adaptive entropy coding to exploit this spatial correlation. In this paper, we propose an alternative approach to exploit this spatial correlation.The proposed scheme also involves a prediction stage. However,we resort to a binary tree based hierarchical decomposition technique to efficiently exploit the spatial correlation. On a set of standard test images, the proposed scheme, using the same predictor as JPEG-LS, achieved an overall compression gain of 2:1% against JPEG-LS.

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DO - 10.1109/PCS.2015.7170074

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BT - PCS 2015 Proceedings

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - USA

T2 - PCS 2015

Y2 - 31 May 2015 through 3 June 2015

ER -

Lossless image coding using binary tree decomposition of prediction residuals

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