Lossless Coding of Light Fields based on 4D Minimum Rate Predictors

Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels,...

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Published in:	IEEE transactions on image processing Vol. 31; p. 1
Main Authors:	Santos, Joao M., Thomaz, Lucas A., Assuncao, Pedro A. A., Da Silva Cruz, Luis A., Tavora, Luis, Faria, Sergio M. M.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	4D partition 4D prediction Algorithms Biomedical imaging Cameras Coding Data structures Encoding Image coding Light field compression Lossless coding Materials requirements planning Medical Imaging Pixels Redundancy Representations Transform coding
ISSN:	1057-7149, 1941-0042, 1941-0042
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Abstract	Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression.
AbstractList	Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression. Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression.Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression.
Author	Assuncao, Pedro A. A. Santos, Joao M. Tavora, Luis Thomaz, Lucas A. Da Silva Cruz, Luis A. Faria, Sergio M. M.
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SubjectTerms	4D partition 4D prediction Algorithms Biomedical imaging Cameras Coding Data structures Encoding Image coding Light field compression Lossless coding Materials requirements planning Medical Imaging Pixels Redundancy Representations Transform coding
Title	Lossless Coding of Light Fields based on 4D Minimum Rate Predictors
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