Efficient Rate-distortion Approximation and Transform Type Selection using Laplacian Operators
Keng-Shih Lu, Antonio J. Ortega, Debargha Mukherjee, Yue Chen · 2018
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 co-efficients 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.