Learned B-frame video compression aims to adopt bi-directional motion
estimation and motion compensation (MEMC) coding for middle frame
reconstruction. However, previous learned approaches often directly extend
neural P-frame codecs to B-frame relying on bi-directional optical-flow
estimation or video frame interpolation. They suffer from inaccurate quantized
motions and inefficient motion compensation. To address these issues, we
propose a simple yet effective structure called Interpolation-driven B-frame
Video Compression (IBVC). Our approach only involves two major operations:
video frame interpolation and artifact reduction compression. IBVC introduces a
bit-rate free MEMC based on interpolation, which avoids optical-flow
quantization and additional compression distortions. Later, to reduce duplicate
bit-rate consumption and focus on unaligned artifacts, a residual guided
masking encoder is deployed to adaptively select the meaningful contexts with
interpolated multi-scale dependencies. In addition, a conditional
spatio-temporal decoder is proposed to eliminate location errors and artifacts
instead of using MEMC coding in other methods. The experimental results on
B-frame coding demonstrate that IBVC has significant improvements compared to
the relevant state-of-the-art methods. Meanwhile, our approach can save bit
rates compared with the random access (RA) configuration of H.266 (VTM). The
code will be available at this https URL

通过使用两个主要操作：视频帧插值和压缩，IBVC（插值驱动的 B 帧视频压缩）引入了一种无比特率的双向运动估计与补偿方法，避免了光流量化和额外的压缩失真，通过自适应选择具有插值多尺度依赖的有意义的上下文，减少重复比特率的使用，并通过提出条件的时空解码器消除位置错误和伪影，相较于相关最先进方法，IBVC 在 B 帧编码方面取得了显著的改进，同时与 H.266（VTM）的随机访问（RA）配置相比，我们的方法能节省比特率。

IBVC: 基于插值的 B 帧视频压缩

IBVC: Interpolation-driven B-frame Video Compression

Recently, high-quality video conferencing with fewer transmission bits has
become a very hot and challenging problem. We propose FAIVConf, a specially
designed video compression framework for video conferencing, based on the
effective neural human face generation techniques. FAIVConf brings together
several designs to improve the system robustness in real video conference
scenarios: face-swapping to avoid artifacts in background animation; facial
blurring to decrease transmission bit-rate and maintain the quality of
extracted facial landmarks; and dynamic source update for face view
interpolation to accommodate a large range of head poses. Our method achieves a
significant bit-rate reduction in the video conference and gives much better
visual quality under the same bit-rate compared with H.264 and H.265 coding
schemes.

提出一种名为 FAIVConf 的视频压缩框架，基于有效的神经人脸生成技术实现，通过获取和保持面部特征进行背景动画避免伪影、面部失焦降低传输比特率、动态源更新进行面部视图插值等多种设计，取得了较大的码率降低和更好的视觉质量。

低比特率下基于 AI 的视频会议人脸增强技术

FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate

Lossy image and video compression algorithms yield visually annoying
artifacts including blocking, blurring, and ringing, especially at low
bit-rates. To reduce these artifacts, post-processing techniques have been
extensively studied. Recently, inspired by the great success of convolutional
neural network (CNN) in computer vision, some researches were performed on
adopting CNN in post-processing, mostly for JPEG compressed images. In this
paper, we present a CNN-based post-processing algorithm for High Efficiency
Video Coding (HEVC), the state-of-the-art video coding standard. We redesign a
Variable-filter-size Residue-learning CNN (VRCNN) to improve the performance
and to accelerate network training. Experimental results show that using our
VRCNN as post-processing leads to on average 4.6% bit-rate reduction compared
to HEVC baseline. The VRCNN outperforms previously studied networks in
achieving higher bit-rate reduction, lower memory cost, and multiplied
computational speedup.

本文提出了一种基于卷积神经网络的后处理算法用于高效视频编码，相较于 HEVC 基线平均比特率减少了 4.6％，并在实现更高的比特率降低，更低的内存成本和更快的计算速度方面优于以前研究的网络。