Original article was published on Deep Learning on Medium
Reading: LFHI & LFSD & LFMD Using AK-CNN — Asymmetric-Kernel CNN (Fast HEVC Prediction)
Outperforms Liu TIP’16 and ETH-CNN & ETH-LSTM, Smaller Model Size Than Li ICME’17, 75.2% Time Reduction Under All-Intra Configuration
In this paper, Asymmetric-Kernel CNN (AK-CNN), by University of Science and Technology of China, is presented. In this paper:
- Asymmetric horizontal and vertical convolution kernels are designed to precisely extract the texture features of each block with much lower complexity.
- The confidence threshold decision scheme is designed in the PU partition part to achieve the best trade-off between the coding performance and complexity reduction.
- This AK-CNN is used for fast size decision as well as fast mode decision, namely Learned Fast Size Decision (LFSD) and Learned Fast Mode Decision (LFMD), respectively. With both used, it becomes a Learned Fast HEVC Intra (LFHI) Coding Scheme.
LFSD is firstly published in 2019 ISCAS and then combining with LFMD to form LFHI, it is published in 2020 TIP where TIP has a high impact factor of 6.79. (Sik-Ho Tsang @ Medium)