The Versatile Video Coding (VVC) standard has been recently finalized by the
Joint Video Exploration Team (JVET). Compared to the High Efficiency Video
Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in
terms of Bjontegaard Delta-Rate (BD-rate), at the cost of a 10-fold increase in
encoding complexity. In this paper, we propose a method based on Convolutional
Neural Network (CNN) to speed up the inter partitioning process in VVC.
Firstly, a novel representation for the quadtree with nested multi-type tree
(QTMT) partition is introduced, derived from the partition path. Secondly, we
develop a U-Net-based CNN taking a multi-scale motion vector field as input at
the Coding Tree Unit (CTU) level. The purpose of CNN inference is to predict
the optimal partition path during the Rate-Distortion Optimization (RDO)
process. To achieve this, we divide CTU into grids and predict the Quaternary
Tree (QT) depth and Multi-type Tree (MT) split decisions for each cell of the
grid. Thirdly, an efficient partition pruning algorithm is introduced to employ
the CNN predictions at each partitioning level to skip RDO evaluations of
unnecessary partition paths. Finally, an adaptive threshold selection scheme is
designed, making the trade-off between complexity and efficiency scalable.
Experiments show that the proposed method can achieve acceleration ranging from
16.5% to 60.2% under the RandomAccess Group Of Picture 32 (RAGOP32)
configuration with a reasonable efficiency drop ranging from 0.44% to 4.59% in
terms of BD-rate, which surpasses other state-of-the-art solutions.
Additionally, our method stands out as one of the lightest approaches in the
field, which ensures its applicability to other encoders.

提出了一种基于卷积神经网络（CNN）的方法，通过预测 VVC 中的最佳分区路径来加速分区过程。在实验中，该方法在 BD-rate 方面取得了 0.44％至 4.59％的效率损失范围内，同时加速范围从 16.5％到 60.2％，超过了其他最先进的解决方案，并且是该领域中最轻量级的方法之一。

基于 CNN 的 VVC 快速帧间分区动态路径预测

CNN-based Prediction of Partition Path for VVC Fast Inter Partitioning  Using Motion Fields

Prevalent predictive coding-based video compression methods rely on a heavy
encoder to reduce the temporal redundancy, which makes it challenging to deploy
them on resource-constrained devices. Meanwhile, as early as the 1970s,
distributed source coding theory has indicated that independent encoding and
joint decoding with side information (SI) can achieve high-efficient
compression of correlated sources. This has inspired a distributed coding
architecture aiming at reducing the encoding complexity. However, traditional
distributed coding methods suffer from a substantial performance gap to
predictive coding ones. Inspired by the great success of learning-based
compression, we propose the first end-to-end distributed deep video compression
framework to improve the rate-distortion performance. A key ingredient is an
effective SI generation module at the decoder, which helps to effectively
exploit inter-frame correlations without computation-intensive encoder-side
motion estimation and compensation. Experiments show that our method
significantly outperforms conventional distributed video coding and H.264.
Meanwhile, it enjoys 6-7x encoding speedup against DVC [1] with comparable
compression performance. Code is released at
this https URL.

论文提出了一种基于深度学习的分布式视频编码架构，通过在解码器中使用有效的辅助信息生成模块，成功利用帧间相关性以提高压缩效率，同时，在编码速度相同的情况下，研究结果优于传统的分布式编码和基于 H.264 标准的压缩方法。

分布式编码架构的低复杂度深度视频压缩

Low-complexity Deep Video Compression with A Distributed Coding Architecture

High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the
proceeding H.264 standard but at the expense of extremely high encoding
complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a
large proportion of the HEVC encoding complexity, due to the bruteforce search
for rate-distortion optimization (RDO). Therefore, this paper proposes a deep
learning approach to predict the CU partition for reducing the HEVC complexity
at both intra- and inter-modes, which is based on convolutional neural network
(CNN) and long- and short-term memory (LSTM) network. First, we establish a
large-scale database including substantial CU partition data for HEVC intra-
and inter-modes. This enables deep learning on the CU partition. Second, we
represent the CU partition of an entire coding tree unit (CTU) in the form of a
hierarchical CU partition map (HCPM). Then, we propose an early-terminated
hierarchical CNN (ETH-CNN) for learning to predict the HCPM. Consequently, the
encoding complexity of intra-mode HEVC can be drastically reduced by replacing
the brute-force search with ETH-CNN to decide the CU partition. Third, an
early-terminated hierarchical LSTM (ETH-LSTM) is proposed to learn the temporal
correlation of the CU partition. Then, we combine ETH-LSTM and ETH-CNN to
predict the CU partition for reducing the HEVC complexity for inter-mode.
Finally, experimental results show that our approach outperforms other
state-of-the-art approaches in reducing the HEVC complexity at both intra- and
inter-modes.

本研究提出了一种基于深度学习的方法，用于预测视频编码中的 CU 分区，以降低 HEVC 的复杂度，并在实验中取得了优于现有方法的效果。