Details
| Original language | English |
|---|---|
| Title of host publication | 2016 Picture Coding Symposium |
| Subtitle of host publication | PCS 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781509059669 |
| Publication status | Published - Apr 2017 |
| Event | 2016 Picture Coding Symposium, PCS 2016 - Nuremberg, Germany Duration: 4 Dec 2016 → 7 Dec 2016 |
Publication series
| Name | 2016 Picture Coding Symposium, PCS 2016 |
|---|
Abstract
The High Efficiency Video Coding standard and its screen content coding extension provide superior coding efficiency compared to predecessor standards. However, this coding efficiency is achieved at the expense of very complex encoders. One major complexity driver is the comprehensive rate distortion (RD) optimization. In this paper, we present a deep learning-based encoder control which replaces the conventional RD optimization for the intra prediction mode with deep convolutional neural network (CNN) classifiers. Thereby, we save the RD optimization complexity. Our classifiers operate independently of any encoder decisions and reconstructed sample values. Thus, no additional systematic latency is introduced. Furthermore, the loss in coding efficiency is negligible with an average value of 0.52% over HM-16.6+SCM-5.2.
ASJC Scopus subject areas
- Engineering(all)
- Media Technology
- Computer Science(all)
- Signal Processing
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2016 Picture Coding Symposium: PCS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7906399 (2016 Picture Coding Symposium, PCS 2016).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Deep learning-based intra prediction mode decision for HEVC
AU - Laude, Thorsten
AU - Ostermann, Jörn
PY - 2017/4
Y1 - 2017/4
N2 - The High Efficiency Video Coding standard and its screen content coding extension provide superior coding efficiency compared to predecessor standards. However, this coding efficiency is achieved at the expense of very complex encoders. One major complexity driver is the comprehensive rate distortion (RD) optimization. In this paper, we present a deep learning-based encoder control which replaces the conventional RD optimization for the intra prediction mode with deep convolutional neural network (CNN) classifiers. Thereby, we save the RD optimization complexity. Our classifiers operate independently of any encoder decisions and reconstructed sample values. Thus, no additional systematic latency is introduced. Furthermore, the loss in coding efficiency is negligible with an average value of 0.52% over HM-16.6+SCM-5.2.
AB - The High Efficiency Video Coding standard and its screen content coding extension provide superior coding efficiency compared to predecessor standards. However, this coding efficiency is achieved at the expense of very complex encoders. One major complexity driver is the comprehensive rate distortion (RD) optimization. In this paper, we present a deep learning-based encoder control which replaces the conventional RD optimization for the intra prediction mode with deep convolutional neural network (CNN) classifiers. Thereby, we save the RD optimization complexity. Our classifiers operate independently of any encoder decisions and reconstructed sample values. Thus, no additional systematic latency is introduced. Furthermore, the loss in coding efficiency is negligible with an average value of 0.52% over HM-16.6+SCM-5.2.
UR - http://www.scopus.com/inward/record.url?scp=85019423425&partnerID=8YFLogxK
U2 - 10.1109/pcs.2016.7906399
DO - 10.1109/pcs.2016.7906399
M3 - Conference contribution
AN - SCOPUS:85019423425
T3 - 2016 Picture Coding Symposium, PCS 2016
BT - 2016 Picture Coding Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 Picture Coding Symposium, PCS 2016
Y2 - 4 December 2016 through 7 December 2016
ER -