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Monteiro, R. J. S., Nunes, P. J. L., Faria, S. M. M. & Rodrigues, N. M. M. (2018). Light field image coding using high order prediction training. In 26th European Signal Processing Conference, EUSIPCO 2018. (pp. 1845-1849). Roma: IEEE.

R. J. Monteiro et al.,  "Light field image coding using high order prediction training", in 26th European Signal Processing Conf., EUSIPCO 2018, Roma, IEEE, 2018, pp. 1845-1849

@inproceedings{monteiro2018_1765163502530,
	author = "Monteiro, R. J. S. and Nunes, P. J. L. and Faria, S. M. M. and Rodrigues, N. M. M.",
	title = "Light field image coding using high order prediction training",
	booktitle = "26th European Signal Processing Conference, EUSIPCO 2018",
	year = "2018",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.23919/EUSIPCO.2018.8553150",
	pages = "1845-1849",
	publisher = "IEEE",
	address = "Roma",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/8553150"
}

TY  - CPAPER
TI  - Light field image coding using high order prediction training
T2  - 26th European Signal Processing Conference, EUSIPCO 2018
AU  - Monteiro, R. J. S.
AU  - Nunes, P. J. L.
AU  - Faria, S. M. M.
AU  - Rodrigues, N. M. M.
PY  - 2018
SP  - 1845-1849
SN  - 2076-1465
DO  - 10.23919/EUSIPCO.2018.8553150
CY  - Roma
UR  - https://ieeexplore.ieee.org/document/8553150
AB  - This paper proposes a new method for light field image coding relying on a high order prediction mode based on a training algorithm. The proposed approach is applied as an Intra prediction method based on a two-stage block-wise high order prediction model that supports geometric transformations up to eight degrees of freedom. Light field images comprise an array of micro-images that are related by complex perspective deformations that cannot be efficiently compensated by state-of-the-art image coding techniques, which are usually based on low order translational prediction models. The proposed prediction mode is able to exploit the non-local spatial redundancy introduced by light field image structure and a training algorithm is applied on different micro-images that are available in the reference region aiming at reducing the amount of signaling data sent to the receiver. The training direction that generates the most efficient geometric transformation for the current block is determined in the encoder side and signaled to the decoder using an index. The decoder is therefore able to repeat the high order prediction training to generate the desired geometric transformation. Experimental results show bitrate savings up to 12.57% and 50.03% relatively to a light field image coding solution based on low order prediction without training and HEVC, respectively. 
ER  -