Decoding remote sensing images to achieve high perceptual quality,
particularly at low bitrates, remains a significant challenge. To address this
problem, we propose the invertible neural network-based remote sensing image
compression (INN-RSIC) method. Specifically, we capture compression distortion
from an existing image compression algorithm and encode it as a set of
Gaussian-distributed latent variables via INN. This ensures that the
compression distortion in the decoded image becomes independent of the ground
truth. Therefore, by leveraging the inverse mapping of INN, we can input the
decoded image along with a set of randomly resampled Gaussian distributed
variables into the inverse network, effectively generating enhanced images with
better perception quality. To effectively learn compression distortion, channel
expansion, Haar transformation, and invertible blocks are employed to construct
the INN. Additionally, we introduce a quantization module (QM) to mitigate the
impact of format conversion, thus enhancing the framework's generalization and
improving the perceptual quality of enhanced images. Extensive experiments
demonstrate that our INN-RSIC significantly outperforms the existing
state-of-the-art traditional and deep learning-based image compression methods
in terms of perception quality.

通过可逆神经网络，我们提出了 INN-RSIC 方法，用于解码远程感应图像并实现高感知质量，在低比特率下尤为重要。我们采用现有图像压缩算法捕捉压缩失真，并通过 INN 将其编码为一组高斯分布的潜在变量，使解码图像中的压缩失真与真实图像无关。因此，通过利用 INN 的逆映射，我们可以将解码图像以及一组随机重采样的高斯分布变量输入到逆网络中，有效生成感知质量更好的增强图像。我们通过引入量化模块（QM）来缓解格式转换的影响，从而提高框架的泛化能力和增强图像的感知质量。广泛的实验表明，我们的 INN-RSIC 在感知质量方面明显优于现有的传统和基于深度学习的图像压缩方法。

通过可逆神经网络增强遥感图像压缩中的感知质量

Enhancing Perception Quality in Remote Sensing Image Compression via  Invertible Neural Network

Recent years have witnessed an exponential increase in the demand for face
video compression, and the success of artificial intelligence has expanded the
boundaries beyond traditional hybrid video coding. Generative coding approaches
have been identified as promising alternatives with reasonable perceptual
rate-distortion trade-offs, leveraging the statistical priors of face videos.
However, the great diversity of distortion types in spatial and temporal
domains, ranging from the traditional hybrid coding frameworks to generative
models, present grand challenges in compressed face video quality assessment
(VQA). In this paper, we introduce the large-scale Compressed Face Video
Quality Assessment (CFVQA) database, which is the first attempt to
systematically understand the perceptual quality and diversified compression
distortions in face videos. The database contains 3,240 compressed face video
clips in multiple compression levels, which are derived from 135 source videos
with diversified content using six representative video codecs, including two
traditional methods based on hybrid coding frameworks, two end-to-end methods,
and two generative methods. In addition, a FAce VideO IntegeRity (FAVOR) index
for face video compression was developed to measure the perceptual quality,
considering the distinct content characteristics and temporal priors of the
face videos. Experimental results exhibit its superior performance on the
proposed CFVQA dataset. The benchmark is now made publicly available at:
this https URL

本文介绍了大规模压缩人脸视频质量评估数据库的构建和性能评估，该数据库被认为是系统地理解人脸视频中感知质量和多样化压缩畸变的第一次尝试，同时还开发了 FAVOR 指数来度量人脸视频的感知质量。