Lossless image coding using binary tree decomposition of prediction residuals

Mortuza Ali, Manzur Murshed, Shampa Shahriyar, Manoranjan Paul

Research output: Book chapter/Published conference paperConference paper

2 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 languageEnglish
Title of host publicationPCS 2015 Proceedings
Subtitle of host publication2015 Picture Coding Symposium (PCS) with 2015 Packet Video Workshop (PV)
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages194-198
Number of pages5
ISBN (Electronic)9781479977833
DOIs
Publication statusPublished - 2015
EventPCS 2015: 31st Picture Coding Symposium - Hotel Pullman Reef Hotel Casino, Cairns, Australia
Duration: 31 May 201503 Jun 2015
http://www.pcs2015.org/

Conference

ConferencePCS 2015
CountryAustralia
CityCairns
Period31/05/1503/06/15
OtherThe 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

Fingerprint

Binary trees
Image coding
Decomposition
Entropy
Image compression

Cite this

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) (pp. 194-198). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PCS.2015.7170074
Ali, Mortuza ; Murshed, Manzur ; Shahriyar, Shampa ; Paul, Manoranjan. / Lossless image coding using binary tree decomposition of prediction residuals. PCS 2015 Proceedings: 2015 Picture Coding Symposium (PCS) with 2015 Packet Video Workshop (PV). USA : IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 194-198
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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.",
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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.",
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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, 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; Paul, Manoranjan.

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 paperConference paper

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AU - Murshed, Manzur

AU - Shahriyar, Shampa

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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.

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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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Ali M, Murshed M, Shahriyar S, Paul M. 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). USA: IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 194-198 https://doi.org/10.1109/PCS.2015.7170074