Name
Title | ||
|---|---|---|
Joint Rate Distortion Optimization with CNN-based In-Loop Filter For Hybrid Video Coding |
Abstract | ||
|---|---|---|
Existing deep learning based approaches for coding artifact removal mainly focus on the relationship between reconstruction and original signals. Although the CNN-based in-loop filtering reveals significant potential, the filtering procedure is separated from the rate distortion optimization (RDO) in the block-based hybrid framework. In this paper, we propose a joint rate distortion optimization scheme [1] with CNN-based in-loop filter for the Versatile Video Coding (VVC), with the goal of excavating the potential of in-loop filtering. In particular, a joint rate distortion optimization is presented, to consider the CNN-based in-loop filtering in the coding unit. More specifically, the CNN-based in-loop filter is involved in the partitioning mode selection, and meanwhile the prediction mode determination process remains unchanged. The proposed joint optimization is only applied to the CUs, whose height and width are no larger than 64. To keep the encoding complexity as low as possible, a light-weight version of CNN-based model is provided to facilitate the encoding optimization. As shown in Table 1, the proposed method brings 9.61% and 12.81% BD-Rate gains for luma component under all intra and random access configurations, respectively, on VTM-11.0-nnvc platform. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/DCC52660.2022.00073 | 2022 Data Compression Conference (DCC) |
Keywords | DocType | ISSN |
Versatile Video Coding,Loop Filter,Convolutional Neural Network,Deep Learning | Conference | 1068-0314 |
ISBN | Citations | PageRank |
978-1-6654-7894-6 | 0 | 0.34 |
References | Authors | |
0 | 4 | |