Details
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE International Conference on Consumer Electronics (ICCE) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 317-320 |
| Number of pages | 4 |
| ISBN (electronic) | 9781479975426 |
| Publication status | Published - 23 Mar 2015 |
| Event | 2015 IEEE International Conference on Consumer Electronics, ICCE 2015 - Las Vegas, United States Duration: 9 Jan 2015 → 12 Jan 2015 |
Abstract
This paper presents a fast Super-Resolution (SR) algorithm based on a selective patch processing. Motivated by the observation that some regions of images are smooth and unfocused and can be properly upscaled with fast interpolation methods, we locally estimate the probability of performing a degradation-free upscaling. Our proposed framework explores the usage of supervised machine learning techniques and tackles the problem using binary boosted tree classifiers. The applied upscaler is chosen based on the obtained probabilities: (1) A fast upscaler (e.g. bicubic interpolation) for those regions which are smooth or (2) a linear regression SR algorithm for those which are ill-posed. The proposed strategy accelerates SR by only processing the regions which benefit from it, thus not compromising quality. Furthermore all the algorithms composing the pipeline are naturally parallelizable and further speed-ups could be obtained.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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2015 IEEE International Conference on Consumer Electronics (ICCE). Institute of Electrical and Electronics Engineers Inc., 2015. p. 317-320 7066429.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Accelerating Super-Resolution for 4K upscaling
AU - Perez-Pellitero, Eduardo
AU - Salvador, Jordi
AU - Ruiz-Hidalgo, Javier
AU - Rosenhahn, Bodo
PY - 2015/3/23
Y1 - 2015/3/23
N2 - This paper presents a fast Super-Resolution (SR) algorithm based on a selective patch processing. Motivated by the observation that some regions of images are smooth and unfocused and can be properly upscaled with fast interpolation methods, we locally estimate the probability of performing a degradation-free upscaling. Our proposed framework explores the usage of supervised machine learning techniques and tackles the problem using binary boosted tree classifiers. The applied upscaler is chosen based on the obtained probabilities: (1) A fast upscaler (e.g. bicubic interpolation) for those regions which are smooth or (2) a linear regression SR algorithm for those which are ill-posed. The proposed strategy accelerates SR by only processing the regions which benefit from it, thus not compromising quality. Furthermore all the algorithms composing the pipeline are naturally parallelizable and further speed-ups could be obtained.
AB - This paper presents a fast Super-Resolution (SR) algorithm based on a selective patch processing. Motivated by the observation that some regions of images are smooth and unfocused and can be properly upscaled with fast interpolation methods, we locally estimate the probability of performing a degradation-free upscaling. Our proposed framework explores the usage of supervised machine learning techniques and tackles the problem using binary boosted tree classifiers. The applied upscaler is chosen based on the obtained probabilities: (1) A fast upscaler (e.g. bicubic interpolation) for those regions which are smooth or (2) a linear regression SR algorithm for those which are ill-posed. The proposed strategy accelerates SR by only processing the regions which benefit from it, thus not compromising quality. Furthermore all the algorithms composing the pipeline are naturally parallelizable and further speed-ups could be obtained.
UR - http://www.scopus.com/inward/record.url?scp=84936100194&partnerID=8YFLogxK
U2 - 10.1109/icce.2015.7066429
DO - 10.1109/icce.2015.7066429
M3 - Conference contribution
AN - SCOPUS:84936100194
SP - 317
EP - 320
BT - 2015 IEEE International Conference on Consumer Electronics (ICCE)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE International Conference on Consumer Electronics, ICCE 2015
Y2 - 9 January 2015 through 12 January 2015
ER -