# Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

arXiv: https://arxiv.org/abs/2205.03587 · HTML: https://mlsanity.com/paper/2205.03587/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 64 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 5 of 5)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Reducing Complexity of HEVC: A Deep Learning Approach](https://mlsanity.com/paper/1710.01218/) (2017)
- [DeepQTMT: A Deep Learning Approach for Fast QTMT-based CU Partition of Intra-mode VVC](https://mlsanity.com/paper/2006.13125/) (2020)
- [CNN-based driving of block partitioning for intra slices encoding](https://mlsanity.com/paper/2011.06691/) (2020)

## Built on by (most cited first, 3 of 3)

- [PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction](https://mlsanity.com/paper/2305.11421/) (2023)
- [CWRNN-INVR: A Coupled WarpRNN based Implicit Neural Video Representation](https://mlsanity.com/paper/2604.06564/) (2026)
- [Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation](https://mlsanity.com/paper/2502.15188/) (2025)

## Abstract (arXiv)

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatialtemporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.