Loading [MathJax]/extensions/MathMenu.js
Learned Rate-Distortion Cost Prediction for Ultrafast Screen Content Intra Coding | IEEE Journals & Magazine | IEEE Xplore

Learned Rate-Distortion Cost Prediction for Ultrafast Screen Content Intra Coding


Abstract:

As online collaborations become more prevalent, screen content has become increasingly important in real-time video communications. To reduce communication costs, the H.2...Show More

Abstract:

As online collaborations become more prevalent, screen content has become increasingly important in real-time video communications. To reduce communication costs, the H.265/HEVC standard introduced the Screen Content Coding (SCC) extension, which achieves significant bits savings but comes with a higher encoding complexity. There is a need for ultrafast SCC encoding to meet the demands of real-time applications. Our key idea is to predict the rate-distortion (RD) cost of each possible coding unit under each possible mode, rather than performing actual coding to obtain the RD cost. Specifically, we construct neural networks to predict RD costs for intra prediction, palette, and normal intra block copy (IBC) modes. For IBC merge mode, we conduct motion compensation trials and use a linear regression network for prediction. Using the predicted RD costs, we create a partition-mode map set that determines not only block partitioning but also optimal modes, significantly reducing encoding complexity. Our experimental results demonstrate that our method achieves a more than 90% reduction in encoding time with an average 9.4% BD-rate increase compared to the HEVC-SCC reference software in the all-intra configuration.
Page(s): 1976 - 1980
Date of Publication: 18 July 2023

ISSN Information:

Funding Agency:


References

References is not available for this document.