Image Quality Assessment (IQA) is essential in various Computer Vision tasks
such as image deblurring and super-resolution. However, most IQA methods
require reference images, which are not always available. While there are some
reference-free IQA metrics, they have limitations in simulating human
perception and discerning subtle image quality variations. We hypothesize that
the JPEG quality factor is representatives of image quality measurement, and a
well-trained neural network can learn to accurately evaluate image quality
without requiring a clean reference, as it can recognize image degradation
artifacts based on prior knowledge. Thus, we developed a reference-free quality
evaluation network, dubbed "Quality Factor (QF) Predictor", which does not
require any reference. Our QF Predictor is a lightweight, fully convolutional
network comprising seven layers. The model is trained in a self-supervised
manner: it receives JPEG compressed image patch with a random QF as input, is
trained to accurately predict the corresponding QF. We demonstrate the
versatility of the model by applying it to various tasks. First, our QF
Predictor can generalize to measure the severity of various image artifacts,
such as Gaussian Blur and Gaussian noise. Second, we show that the QF Predictor
can be trained to predict the undersampling rate of images reconstructed from
Magnetic Resonance Imaging (MRI) data.

通过开发一种无需参考图像的轻量级卷积神经网络，我们证明了 JPEG 质量因子能够代表图像质量度量，并能够准确评估图像质量，而无需参考图像。我们的模型能够应用于不同任务，包括度量各种图像伪影的严重程度和预测从磁共振成像（MRI）数据重建的图像的欠采样率。

无参考图像质量度量方法用于降解和重构物件

Reference-Free Image Quality Metric for Degradation and Reconstruction  Artifacts

Recently, the area of adversarial attacks on image quality metrics has begun
to be explored, whereas the area of defences remains under-researched. In this
study, we aim to cover that case and check the transferability of adversarial
purification defences from image classifiers to IQA methods. In this paper, we
apply several widespread attacks on IQA models and examine the success of the
defences against them. The purification methodologies covered different
preprocessing techniques, including geometrical transformations, compression,
denoising, and modern neural network-based methods. Also, we address the
challenge of assessing the efficacy of a defensive methodology by proposing
ways to estimate output visual quality and the success of neutralizing attacks.
Defences were tested against attack on three IQA metrics -- Linearity, MetaIQA
and SPAQ. The code for attacks and defences is available at: (link is hidden
for a blind review).

本研究探索了关于图像质量度量的对抗性攻击领域，并研究了防御方面的相关问题，试图检查将对抗净化防御方法从图像分类器应用到图像质量度量方法的可行性。通过对几种广泛被应用于图像质量度量模型的攻击进行实验，结合几种不同的预处理技术（包括几何变换、压缩、去噪和现代神经网络方法），评估了防御方法的成功与对抗性攻击处理的视觉质量输出和成功率之间关联的挑战。并在三个图像质量度量评估指标（线性度、元 IQA 和 SPAQ）上进行了测试。

无参考图像质量度量的对抗性提纯：适用性研究与新方法

Adversarial purification for no-reference image-quality metrics:  applicability study and new methods

This paper reports on the NTIRE 2021 challenge on perceptual image quality
assessment (IQA), held in conjunction with the New Trends in Image Restoration
and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image
processing technology, perceptual image processing algorithms based on
Generative Adversarial Networks (GAN) have produced images with more realistic
textures. These output images have completely different characteristics from
traditional distortions, thus pose a new challenge for IQA methods to evaluate
their visual quality. In comparison with previous IQA challenges, the training
and testing datasets in this challenge include the outputs of perceptual image
processing algorithms and the corresponding subjective scores. Thus they can be
used to develop and evaluate IQA methods on GAN-based distortions. The
challenge has 270 registered participants in total. In the final testing stage,
13 participating teams submitted their models and fact sheets. Almost all of
them have achieved much better results than existing IQA methods, while the
winning method can demonstrate state-of-the-art performance.

本文介绍了 NTIRE2021 挑战赛，关于基于生成对抗网络的知觉图像质量评估的主题和研究领域，挑战的训练和测试数据集包括感知图像处理算法的输出和相应的主观得分，旨在开发和评估 GAN 失真的图像质量评估方法。在比较以前的 IQA 挑战赛之后，参赛的 13 支队伍提交了他们的模型和事实表，几乎所有队伍都比现有的 IQA 方法取得了更好的结果，其中获胜的方法表现出最先进的性能。