Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9798350359855 |
| Publication status | Published - 2023 |
| Event | 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023 - Jeju, Korea, Republic of Duration: 4 Dec 2023 → 7 Dec 2023 |
Abstract
In this work, we propose a hybrid learning-based method for layered spatial scalability. Our framework consists of a base layer (BL), which encodes a spatially downsampled representation of the input video using Versatile Video Coding (VVC), and a learning-based enhancement layer (EL), which conditionally encodes the original video signal. The EL is conditioned by two fused prediction signals: A spatial inter-layer prediction signal, that is generated by spatially upsampling the output of the BL using super-resolution, and a temporal inter-frame prediction signal, that is generated by decoder-side motion compensation without signaling any motion vectors. We show that our method outperforms LCEVC and has comparable performance to full-resolution VVC for high-resolution content, while still offering scalability.
Keywords
- conditional coding, scalable coding, spatial scalability, video coding, VVC
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Hardware and Architecture
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Media Technology
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2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023. Institute of Electrical and Electronics Engineers Inc., 2023.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Learning-Based Scalable Video Coding with Spatial and Temporal Prediction
AU - Benjak, Martin
AU - Chen, Yi Hsin
AU - Peng, Wen Hsiao
AU - Ostermann, Jorn
PY - 2023
Y1 - 2023
N2 - In this work, we propose a hybrid learning-based method for layered spatial scalability. Our framework consists of a base layer (BL), which encodes a spatially downsampled representation of the input video using Versatile Video Coding (VVC), and a learning-based enhancement layer (EL), which conditionally encodes the original video signal. The EL is conditioned by two fused prediction signals: A spatial inter-layer prediction signal, that is generated by spatially upsampling the output of the BL using super-resolution, and a temporal inter-frame prediction signal, that is generated by decoder-side motion compensation without signaling any motion vectors. We show that our method outperforms LCEVC and has comparable performance to full-resolution VVC for high-resolution content, while still offering scalability.
AB - In this work, we propose a hybrid learning-based method for layered spatial scalability. Our framework consists of a base layer (BL), which encodes a spatially downsampled representation of the input video using Versatile Video Coding (VVC), and a learning-based enhancement layer (EL), which conditionally encodes the original video signal. The EL is conditioned by two fused prediction signals: A spatial inter-layer prediction signal, that is generated by spatially upsampling the output of the BL using super-resolution, and a temporal inter-frame prediction signal, that is generated by decoder-side motion compensation without signaling any motion vectors. We show that our method outperforms LCEVC and has comparable performance to full-resolution VVC for high-resolution content, while still offering scalability.
KW - conditional coding
KW - scalable coding
KW - spatial scalability
KW - video coding
KW - VVC
UR - http://www.scopus.com/inward/record.url?scp=85184853773&partnerID=8YFLogxK
U2 - 10.1109/VCIP59821.2023.10402677
DO - 10.1109/VCIP59821.2023.10402677
M3 - Conference contribution
AN - SCOPUS:85184853773
BT - 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023
Y2 - 4 December 2023 through 7 December 2023
ER -