In the latest years, videoconferencing has taken a fundamental role in
interpersonal relations, both for personal and business purposes. Lossy video
compression algorithms are the enabling technology for videoconferencing, as
they reduce the bandwidth required for real-time video streaming. However,
lossy video compression decreases the perceived visual quality. Thus, many
techniques for reducing compression artifacts and improving video visual
quality have been proposed in recent years. In this work, we propose a novel
GAN-based method for compression artifacts reduction in videoconferencing.
Given that, in this context, the speaker is typically in front of the camera
and remains the same for the entire duration of the transmission, we can
maintain a set of reference keyframes of the person from the higher-quality
I-frames that are transmitted within the video stream and exploit them to guide
the visual quality improvement; a novel aspect of this approach is the update
policy that maintains and updates a compact and effective set of reference
keyframes. First, we extract multi-scale features from the compressed and
reference frames. Then, our architecture combines these features in a
progressive manner according to facial landmarks. This allows the restoration
of the high-frequency details lost after the video compression. Experiments
show that the proposed approach improves visual quality and generates
photo-realistic results even with high compression rates. Code and pre-trained
networks are publicly available at
this https URL

我们提出了一种基于 GAN 的方法，用于视频会议中的压缩失真减少，通过提取多尺度特征和结合面部标志的方式，恢复视频压缩后丢失的高频细节，实验证明该方法在高压缩速率下提高了视觉质量并生成了逼真的结果。

基于关键帧的 GAN 在视频会议中的感知质量提升

Perceptual Quality Improvement in Videoconferencing using  Keyframes-based GAN

In recent years, much research has been conducted on image super-resolution
(SR). To the best of our knowledge, however, few SR methods were concerned with
compressed images. The SR of compressed images is a challenging task due to the
complicated compression artifacts, while many images suffer from them in
practice. The intuitive solution for this difficult task is to decouple it into
two sequential but independent subproblems, i.e., compression artifacts
reduction (CAR) and SR. Nevertheless, some useful details may be removed in CAR
stage, which is contrary to the goal of SR and makes the SR stage more
challenging. In this paper, an end-to-end trainable deep convolutional neural
network is designed to perform SR on compressed images (CISRDCNN), which
reduces compression artifacts and improves image resolution jointly.
Experiments on compressed images produced by JPEG (we take the JPEG as an
example in this paper) demonstrate that the proposed CISRDCNN yields
state-of-the-art SR performance on commonly used test images and imagesets. The
results of CISRDCNN on real low quality web images are also very impressive,
with obvious quality enhancement. Further, we explore the application of the
proposed SR method in low bit-rate image coding, leading to better
rate-distortion performance than JPEG.

本文提出了一种基于深度卷积神经网络的端到端训练模型，用于对压缩图像进行超分辨率重建和压缩伪影去除，实验证明该模型在 JPEG 压缩图像上表现出优异的超分辨率性能和比 JPEG 更好的速率 - 失真性能。

使用深度卷积神经网络的压缩图像超分辨率重建

CISRDCNN: Super-resolution of compressed images using deep convolutional  neural networks

We consider the compression artifacts reduction problem, where a compressed
image is transformed into an artifact-free image. Recent approaches for this
problem typically train a one-to-one mapping using a per-pixel $L_2$ loss
between the outputs and the ground-truths. We point out that these approaches
used to produce overly smooth results, and PSNR doesn't reflect their real
performance. In this paper, we propose a one-to-many network, which measures
output quality using a perceptual loss, a naturalness loss, and a JPEG loss. We
also avoid grid-like artifacts during deconvolution using a "shift-and-average"
strategy. Extensive experimental results demonstrate the dramatic visual
improvement of our approach over the state of the arts.

提出了一种使用感知损失，自然损失和 JPEG 损失的网络，同时避免了反卷积过程中出现的网格状伪影，实验证明了该方法在减少图像的压缩伪影方面的显着视觉效果。