LOSSLESS COMPRESSION OF SUBAPERTURE IMAGES USING CONTEXT MODELING

被引：0

作者：

Schiopu, Ionut ^{[1
]}

Gabbouj, Moncef ^{[1
]}

Gotchev, Atanas ^{[1
]}

Hannuksela, Miska M. ^{[2
]}

机构：

[1] Tampere Univ Technol, Lab Signal Proc, Tampere, Finland

[2] Nokia Technol, Tampere, Finland

来源：

2017 3DTV CONFERENCE: THE TRUE VISION - CAPTURE, TRANSMISSION AND DISPLAY OF 3D VIDEO (3DTV-CON) | 2017年

关键词：

Lossless compression; plenoptic image; context modeling; image segmentation; predictive coding;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The paper proposes a method for lossless compression of sub-aperture image stacks obtained by rectifying light-field images captured by a plenoptic camera. We exploit the similarities between two subaperture images using a predictive coding algorithm, where the current view is predicted from one reference view. Context modeling is the main technique used to reduce the image file size. A suitable image segmentation and a template context are used by the context tree algorithm for encoding up to the smallest detail in each subaperture image. Entropy coding is configured by a residual analysis module. The results show improved performance compared to the state-of-the-art encoders.

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页数：4

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