The visual quality of an image is confounded by a number of intertwined
factors including its semantic content, distortion characteristics and
appearance properties such as brightness, contrast, sharpness, and
colourfulness. Distilling high level knowledge about all these quality bearing
attributes is crucial for developing objective Image Quality Assessment
(IQA).While existing solutions have modeled some of these aspects, a
comprehensive solution that involves all these important quality related
attributes has not yet been developed. In this paper, we present a new blind
IQA (BIQA) model termed Self-supervision and Vision-Language supervision Image
QUality Evaluator (SLIQUE) that features a joint vision-language and visual
contrastive representation learning framework for acquiring high level
knowledge about the images semantic contents, distortion characteristics and
appearance properties for IQA. For training SLIQUE, we have developed a
systematic approach to constructing a first of its kind large image database
annotated with all three categories of quality relevant texts. The Text
Annotated Distortion, Appearance and Content (TADAC) database has over 1.6
million images annotated with textual descriptions of their semantic contents,
distortion characteristics and appearance properties. The method for
constructing TADAC and the database itself will be particularly useful for
exploiting vision-language modeling for advanced IQA applications. Extensive
experimental results show that SLIQUE has superior performances over state of
the art, demonstrating the soundness of its design principle and the
effectiveness of its implementation.

本文介绍了一种名为 SLIQUE 的新型盲目图像质量评估 (BIQA) 模型，该模型采用联合视觉 - 语言和视觉对比表示学习框架，通过获取关于图像语义内容、畸变特征和外观属性的高级知识来进行质量评估。为了训练 SLIQUE，我们开发了一种系统的方法来构建一个首个具有所有三类质量相关文本注释的大型图像数据库 TADAC。TADAC 数据库拥有超过 160 万张图像，用文本描述它们的语义内容、畸变特征和外观属性。构建 TADAC 的方法和数据库本身将特别有助于利用视觉 - 语言建模进行高级图像质量评估应用。大量实验结果显示，SLIQUE 在性能上优于现有技术，证明了其设计原理的完整性和实现的有效性。

基于视觉语言建模的图像质量评估：内容、失真和外观

Vision Language Modeling of Content, Distortion and Appearance for Image  Quality Assessment

Image quality assessment (IQA) plays a critical role in selecting
high-quality images and guiding compression and enhancement methods in a series
of applications. The blind IQA, which assesses the quality of in-the-wild
images containing complex authentic distortions without reference images, poses
greater challenges. Existing methods are limited to modeling a uniform
distribution with local patches and are bothered by the gap between low and
high-level visions (caused by widely adopted pre-trained classification
networks). In this paper, we propose a novel IQA method called diffusion
priors-based IQA (DP-IQA), which leverages the prior knowledge from the
pre-trained diffusion model with its excellent powers to bridge semantic gaps
in the perception of the visual quality of images. Specifically, we use
pre-trained stable diffusion as the backbone, extract multi-level features from
the denoising U-Net during the upsampling process at a specified timestep, and
decode them to estimate the image quality score. The text and image adapters
are adopted to mitigate the domain gap for downstream tasks and correct the
information loss caused by the variational autoencoder bottleneck. Finally, we
distill the knowledge in the above model into a CNN-based student model,
significantly reducing the parameter to enhance applicability, with the student
model performing similarly or even better than the teacher model surprisingly.
Experimental results demonstrate that our DP-IQA achieves state-of-the-art
results on various in-the-wild datasets with better generalization capability,
which shows the superiority of our method in global modeling and utilizing the
hierarchical feature clues of diffusion for evaluating image quality.

基于扩散先验的盲目图像质量评估（DP-IQA）方法利用预训练扩散模型的先验知识，通过提取多级特征和解码来估计图像质量得分，实验结果表明我们的方法在各种自然图像数据集上取得了最先进的结果，并展示了我们方法在全局建模和利用扩散的层次特征线索评估图像质量方面的优越性。

DP-IQA：利用扩散先验进行野外盲图像质量评估

DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in  the Wild

With the emergence of image super-resolution (SR) algorithm, how to blindly
evaluate the quality of super-resolution images has become an urgent task.
However, existing blind SR image quality assessment (IQA) metrics merely focus
on visual characteristics of super-resolution images, ignoring the available
scale information. In this paper, we reveal that the scale factor has a
statistically significant impact on subjective quality scores of SR images,
indicating that the scale information can be used to guide the task of blind SR
IQA. Motivated by this, we propose a scale guided hypernetwork framework that
evaluates SR image quality in a scale-adaptive manner. Specifically, the blind
SR IQA procedure is divided into three stages, i.e., content perception,
evaluation rule generation, and quality prediction. After content perception, a
hypernetwork generates the evaluation rule used in quality prediction based on
the scale factor of the SR image. We apply the proposed scale guided
hypernetwork framework to existing representative blind IQA metrics, and
experimental results show that the proposed framework not only boosts the
performance of these IQA metrics but also enhances their generalization
abilities. Source code will be available at this https URL

本文介绍了一种基于尺度引导的超网络框架，用于在盲质量评估过程中以尺度自适应的方式评估超分辨率图像质量。