Blind image quality assessment (BIQA) approaches, while promising for
automating image quality evaluation, often fall short in real-world scenarios
due to their reliance on a generic quality standard applied uniformly across
diverse images. This one-size-fits-all approach overlooks the crucial
perceptual relationship between image content and quality, leading to a 'domain
shift' challenge where a single quality metric inadequately represents various
content types. Furthermore, BIQA techniques typically overlook the inherent
differences in the human visual system among different observers. In response
to these challenges, this paper introduces PICNIQ, an innovative pairwise
comparison framework designed to bypass the limitations of conventional BIQA by
emphasizing relative, rather than absolute, quality assessment. PICNIQ is
specifically designed to assess the quality differences between image pairs.
The proposed framework implements a carefully crafted deep learning
architecture, a specialized loss function, and a training strategy optimized
for sparse comparison settings. By employing psychometric scaling algorithms
like TrueSkill, PICNIQ transforms pairwise comparisons into
just-objectionable-difference (JOD) quality scores, offering a granular and
interpretable measure of image quality. We conduct our research using
comparison matrices from the PIQ23 dataset, which are published in this paper.
Our extensive experimental analysis showcases PICNIQ's broad applicability and
superior performance over existing models, highlighting its potential to set
new standards in the field of BIQA.

本文介绍了 PICNIQ，一种创新的成对比较框架，通过强调相对而非绝对质量评估，克服传统盲图像质量评估方法的局限性。PICNIQ 利用精心设计的深度学习架构、特定的损失函数和稀疏比较设置的训练策略实现了成对图像的质量差异评估，并通过心理测量缩放算法（如 TrueSkill）将成对比较转化为可解释的图像质量评分。通过使用 PIQ23 数据集中的比较矩阵进行广泛的实验分析，我们展示了 PICNIQ 在现有模型上的卓越性能，凸显了其在盲图像质量评估领域中树立新的标准的潜力。