Improving Deep Video Compression by Resolution-adaptive Flow Coding
Abstrak
In the learning based video compression approaches, it is an essential issue to compress pixel-level optical flow maps by developing new motion vector (MV) encoders. In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder. To handle complex or simple motion patterns globally, our frame-level scheme RaFC-frame automatically decides the optimal flow map resolution for each video frame. To cope different types of motion patterns locally, our block-level scheme called RaFC-block can also select the optimal resolution for each local block of motion features. In addition, the rate-distortion criterion is applied to both RaFC-frame and RaFC-block and select the optimal motion coding mode for effective flow coding. Comprehensive experiments on four benchmark datasets HEVC, VTL, UVG and MCL-JCV clearly demonstrate the effectiveness of our overall RaFC framework after combing RaFC-frame and RaFC-block for video compression.
Topik & Kata Kunci
Penulis (14)
Zhihao Hu
Zhenghao Chen
Dong Xu
Guo Lu
Wanli Ouyang
Shuhang Gu College of Software
B. University
China
School of Electrical
Information Engineering
The University of Sydney
Australia
School of Computer ScienceTechnology
Beijing University of Technology
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 140×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1007/978-3-030-58536-5_12
- Akses
- Open Access ✓