The human visual system (HVS) is effective at distinguishing low-quality
images due to its ability to sense the distortion level and the resulting
semantic impact. Prior research focuses on developing dedicated networks based
on the presence and absence of pristine images, respectively, and this results
in limited application scope and potential performance inconsistency when
switching from NR to FR IQA. In addition, most methods heavily rely on spatial
distortion modeling through difference maps or weighted features, and this may
not be able to well capture the correlations between distortion and the
semantic impact it causes. To this end, we aim to design a unified network for
both Full-Reference (FR) and No-Reference (NR) IQA via semantic impact
modeling. Specifically, we employ an encoder to extract multi-level features
from input images. Then a Hierarchical Self-Attention (HSA) module is proposed
as a universal adapter for both FR and NR inputs to model the spatial
distortion level at each encoder stage. Furthermore, considering that
distortions contaminate encoder stages and damage image semantic meaning
differently, a Cross-Scale Cross-Attention (CSCA) module is proposed to examine
correlations between distortion at shallow stages and deep ones. By adopting
HSA and CSCA, the proposed network can effectively perform both FR and NR IQA.
Extensive experiments demonstrate that the proposed simple network is effective
and outperforms the relevant state-of-the-art FR and NR methods on four
synthetic-distorted datasets and three authentic-distorted datasets.

设计了一个用于全参考和无参考图像质量评价的统一网络，通过语义影响建模来提高性能，并且在各种数据集上超越了相关的最先进方法。

UNIQA：面向完全参考和无参考图像质量评估的统一框架

UNIQA: A Unified Framework for Both Full-Reference and No-Reference  Image Quality Assessment

Development of perceptual image quality assessment (IQA) metrics has been of
significant interest to computer vision community. The aim of these metrics is
to model quality of an image as perceived by humans. Recent works in
Full-reference IQA research perform pixelwise comparison between deep features
corresponding to query and reference images for quality prediction. However,
pixelwise feature comparison may not be meaningful if distortion present in
query image is severe. In this context, we explore utilization of no-reference
features in Full-reference IQA task. Our model consists of both full-reference
and no-reference branches. Full-reference branches use both distorted and
reference images, whereas No-reference branch only uses distorted image. Our
experiments show that use of no-reference features boosts performance of image
quality assessment. Our model achieves higher SRCC and KRCC scores than a
number of state-of-the-art algorithms on KADID-10K and PIPAL datasets.

该研究探索在全参考图像质量评估任务中利用无参考特征，通过对比和参考图像的深度特征对失真图像进行像素级对比可能没有意义