TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression

Tutkimustuotosvertaisarvioitu

Standard

Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression. / Helin, Petri; Astola, Pekka; Rao, Bhaskar; Tabus, Ioan.

2016 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2016. IEEE COMPUTER SOCIETY PRESS, 2016.

Tutkimustuotosvertaisarvioitu

Harvard

Helin, P, Astola, P, Rao, B & Tabus, I 2016, Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression. julkaisussa 2016 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2016. IEEE COMPUTER SOCIETY PRESS, 3DTV-CONFERENCE : THE TRUE VISION - CAPTURE, TRANSMISSION AND DISPLAY OF 3D VIDEO, 1/01/00. https://doi.org/10.1109/3DTV.2016.7548953

APA

Helin, P., Astola, P., Rao, B., & Tabus, I. (2016). Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression. teoksessa 2016 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2016 IEEE COMPUTER SOCIETY PRESS. https://doi.org/10.1109/3DTV.2016.7548953

Vancouver

Helin P, Astola P, Rao B, Tabus I. Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression. julkaisussa 2016 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2016. IEEE COMPUTER SOCIETY PRESS. 2016 https://doi.org/10.1109/3DTV.2016.7548953

Author

Helin, Petri ; Astola, Pekka ; Rao, Bhaskar ; Tabus, Ioan. / Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression. 2016 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2016. IEEE COMPUTER SOCIETY PRESS, 2016.

Bibtex - Lataa

@inproceedings{6a5d05aaec6b4fe7b4d4c0050976e9dd,
title = "Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression",
abstract = "This paper studies the lossless compression of rectified light-field images captured by plenoptic cameras, exploiting the high similarity existing between the subaperture images, or views, composing the light-field image. The encoding is predictive, where one sparse predictor is designed for every region of a view, using as regressors the pixels from the already transmitted views. As a first step, consistent segmentations for all subaperture images are constructed, defining the regions as connected components in the quantized depth map of the central view, and then propagating them to all side views. The sparse predictors are able to take into account the small horizontal and vertical disparities between regions in corresponding close-by views and perform optimal least squares interpolation accounting implicitly for fractional disparities. The optimal structure of the sparse predictor is selected for each region based on an implementable description length. The encoding of the views is done sequentially starting from the central view and the scheme produces results better than standard lossless compression methods utilized directly on the full lightfield image or applied to the views in a similar sequential order as our method.",
keywords = "depth map warping, light-field coding, lossless compression, plenoptics, sparse prediction",
author = "Petri Helin and Pekka Astola and Bhaskar Rao and Ioan Tabus",
year = "2016",
month = "8",
day = "22",
doi = "10.1109/3DTV.2016.7548953",
language = "English",
publisher = "IEEE COMPUTER SOCIETY PRESS",
booktitle = "2016 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2016",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression

AU - Helin, Petri

AU - Astola, Pekka

AU - Rao, Bhaskar

AU - Tabus, Ioan

PY - 2016/8/22

Y1 - 2016/8/22

N2 - This paper studies the lossless compression of rectified light-field images captured by plenoptic cameras, exploiting the high similarity existing between the subaperture images, or views, composing the light-field image. The encoding is predictive, where one sparse predictor is designed for every region of a view, using as regressors the pixels from the already transmitted views. As a first step, consistent segmentations for all subaperture images are constructed, defining the regions as connected components in the quantized depth map of the central view, and then propagating them to all side views. The sparse predictors are able to take into account the small horizontal and vertical disparities between regions in corresponding close-by views and perform optimal least squares interpolation accounting implicitly for fractional disparities. The optimal structure of the sparse predictor is selected for each region based on an implementable description length. The encoding of the views is done sequentially starting from the central view and the scheme produces results better than standard lossless compression methods utilized directly on the full lightfield image or applied to the views in a similar sequential order as our method.

AB - This paper studies the lossless compression of rectified light-field images captured by plenoptic cameras, exploiting the high similarity existing between the subaperture images, or views, composing the light-field image. The encoding is predictive, where one sparse predictor is designed for every region of a view, using as regressors the pixels from the already transmitted views. As a first step, consistent segmentations for all subaperture images are constructed, defining the regions as connected components in the quantized depth map of the central view, and then propagating them to all side views. The sparse predictors are able to take into account the small horizontal and vertical disparities between regions in corresponding close-by views and perform optimal least squares interpolation accounting implicitly for fractional disparities. The optimal structure of the sparse predictor is selected for each region based on an implementable description length. The encoding of the views is done sequentially starting from the central view and the scheme produces results better than standard lossless compression methods utilized directly on the full lightfield image or applied to the views in a similar sequential order as our method.

KW - depth map warping

KW - light-field coding

KW - lossless compression

KW - plenoptics

KW - sparse prediction

U2 - 10.1109/3DTV.2016.7548953

DO - 10.1109/3DTV.2016.7548953

M3 - Conference contribution

BT - 2016 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2016

PB - IEEE COMPUTER SOCIETY PRESS

ER -