| Session: | Video Coding |
| Location: | Lecture Room |
| Session Time: | Wednesday, June 27, 13:40 - 15:20 |
| Presentation Time: | Wednesday, June 27, 14:00 - 14:20 |
| Presentation: |
Lecture
|
| Paper Title: |
EFFICIENT RATE-DISTORTION APPROXIMATION AND TRANSFORM TYPE SELECTION USING LAPLACIAN OPERATORS |
| Authors: |
Keng-Shih Lu; University of Southern California, United States | | |
| | Antonio Ortega; University of Southern California, United States | | |
| | Debargha Mukherjee; Google, United States | | |
| | Yue Chen; Google, United States | | |
| Abstract: |
Rate-distortion (RD) optimization is an important tool in many video compression standards and can be used for transform selection. However, this is typically very computationally demanding because a full RD search involves the computation of transform coefficients for each candidate transform. In this paper, we propose an approach that uses sparse Laplacian operators to estimate the RD cost by computing a weighted squared sum of transform coefficients, without having to compute the actual transform coefficients. We demonstrate experimentally how our method can be applied for transform selection. Implemented in the AV1 encoder, our approach yields a significant speed-up in encoding time with a small increase in bitrate. |