Details
| Original language | English |
|---|---|
| Title of host publication | 2021 Picture Coding Symposium, PCS 2021 - Proceedings |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (electronic) | 978-1-6654-2545-2 |
| Publication status | Published - 2021 |
Publication series
| Name | 2021 Picture Coding Symposium, PCS 2021 - Proceedings |
|---|
Abstract
Intra prediction is an essential part of video coding. In this work, two methods are proposed for improving intra prediction. The first contribution is a stochastic contour model for modeling and extrapolation of contours detected in the reference area. A Gaussian process is used for the modeling and a multivariate Gaussian distribution is formulated for the extrapolation. The second contribution is a neural network-based method for sample value prediction. The neural networks are used to process the adjacent reference sample values and the results of contour modeling and extrapolation as input data to generate a prediction of the sample values of the block to be coded. The neural networks were designed with an auto-encoder architecture and trained to minimize the appproximated bit rate of the prediction error. The coding efficiency of the video codec HEVC is increased by up to 5%. Averaged over all 55 test sequences, the All Intra configuration resulted in BD-rates of -0.54% for high bit rates and -1.0% for low bit rates.
ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Media Technology
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2021 Picture Coding Symposium, PCS 2021 - Proceedings. 2021. p. 1-5 9477500 (2021 Picture Coding Symposium, PCS 2021 - Proceedings).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Contour-based Intra Coding Using Gaussian Processes and Neural Networks.
AU - Laude, Thorsten
AU - Ostermann, Jörn
PY - 2021
Y1 - 2021
N2 - Intra prediction is an essential part of video coding. In this work, two methods are proposed for improving intra prediction. The first contribution is a stochastic contour model for modeling and extrapolation of contours detected in the reference area. A Gaussian process is used for the modeling and a multivariate Gaussian distribution is formulated for the extrapolation. The second contribution is a neural network-based method for sample value prediction. The neural networks are used to process the adjacent reference sample values and the results of contour modeling and extrapolation as input data to generate a prediction of the sample values of the block to be coded. The neural networks were designed with an auto-encoder architecture and trained to minimize the appproximated bit rate of the prediction error. The coding efficiency of the video codec HEVC is increased by up to 5%. Averaged over all 55 test sequences, the All Intra configuration resulted in BD-rates of -0.54% for high bit rates and -1.0% for low bit rates.
AB - Intra prediction is an essential part of video coding. In this work, two methods are proposed for improving intra prediction. The first contribution is a stochastic contour model for modeling and extrapolation of contours detected in the reference area. A Gaussian process is used for the modeling and a multivariate Gaussian distribution is formulated for the extrapolation. The second contribution is a neural network-based method for sample value prediction. The neural networks are used to process the adjacent reference sample values and the results of contour modeling and extrapolation as input data to generate a prediction of the sample values of the block to be coded. The neural networks were designed with an auto-encoder architecture and trained to minimize the appproximated bit rate of the prediction error. The coding efficiency of the video codec HEVC is increased by up to 5%. Averaged over all 55 test sequences, the All Intra configuration resulted in BD-rates of -0.54% for high bit rates and -1.0% for low bit rates.
UR - http://www.scopus.com/inward/record.url?scp=85112060824&partnerID=8YFLogxK
U2 - 10.1109/PCS50896.2021.9477500
DO - 10.1109/PCS50896.2021.9477500
M3 - Conference contribution
SN - 978-1-6654-3078-4
T3 - 2021 Picture Coding Symposium, PCS 2021 - Proceedings
SP - 1
EP - 5
BT - 2021 Picture Coding Symposium, PCS 2021 - Proceedings
ER -