The state‐of‐the‐art video coding standard, Versatile Video Coding (VVC) or H.266, has demonstrated its superior coding efficiency over its predecessor HEVC/H.265. In this paper, a novel in‐loop filter based on convolutional neural network (CNN) is illustrated to further improve the coding efficiency over VVC. In this filter, one single NN model is used to process multiple video components simultaneously. In addition, with a quality map generated for each video component as network input, the same single NN model is capable of processing videos in different qualities and resolutions while maintaining coding efficiency, which reduces the overall network complexity significantly. Simulation results show that the proposed approach provides average BD‐rate savings of 6.27%, 18.78% and 20.42% under AI configuration, and average BD-rate savings of 5.18%, 21.95% and 22.13% under RA configuration, respectively for Y, Cb and Cr components.
This paper provides a technical overview of the most probable modes (MPM)-based multiple reference line (M-MRL) intra-picture prediction that was adopted into the Versatile Video Coding (VVC) standard draft at the 12th JVET meeting. M-MRL applies not only the nearest reference line but also farther reference lines to MPMs for intra-picture prediction. The highlighted aspects of the adopted M-MRL scheme include the signaling of the reference line index, discontinuous reference lines, the reference sample construction and prediction for farther reference lines, and the joint reference line and intra mode decisions at encoder side. Experimental results are provided to evaluate the performance of M-MRL on top of the VVC test model VTM-2.0.1 together with an analysis of discontinuous reference lines. The presented M-MRL provides 0.5% bitrate savings for an all intra and 0.2% for a random access configuration on average.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.