Abstract
Image quality assessment often relies on raw opinion scores provided by subjects in subjective experiments, which can be noisy and unreliable. To address this issue, postprocessing procedures such as ITU-R BT.500, ITU-T P.910, and ITU-T P.913 have been standardized to clean up the original opinion scores. These methods use annotator-based statistical priors, but they do not take into account extensive information about the image itself, which limits their performance in less annotated scenarios. Generally speaking, image quality datasets usually contain similar scenes or distortions, and it is inevitable for subjects to compare images to score a reasonable score when scoring. Therefore, In this paper, we proposed Subjective Image Quality Score Preprocessing Method perceptual similarity Subjective Preprocessing (PSP), which exploit the perceptual similarity between images to alleviate subjective bias in less annotated scenarios. Specifically, we model subjective scoring as a conditional probability model based on perceptual similarity with previously scored images, called subconscious reference scoring. The reference images are stored by a neighbor dictionary, which is obtained by a normalized vector dot-product based nearest neighbor search of the images' perceptual depth features. Then the preprocessed score is updated by the exponential moving average (EMA) of the subconscious reference scoring, called similarity regularized EMA. Our experiments on multiple datasets (LIVE, TID2013, CID2013) show that this method can effectively remove the bias of the subjective scores. Additionally, Experiments prove that the Preprocesed dataset can improve the performance of downstream IQA tasks very well.
Abstract (translated)
图像质量评估通常依赖于参与者在主观实验中提供的原始意见得分，这些得分可能嘈杂且不可靠。为解决这个问题，已标准化了诸如ITU-R BT.500、ITU-T P.910和ITU-T P.913等后处理过程，以清理原始意见得分。这些方法基于注释者的统计先验，但它们没有考虑到图像本身 extensive 的信息，这限制了它们在较少注释的场景中的性能。 总的来说，图像质量数据集通常包含相似的场景或畸变，因此当评分时，受试者不可避免地会将与图像进行比较以给出合理的分数。因此，在本文中，我们提出了 Subjective Image Quality Score Preprocessing Method perceptual similarity Subjective Preprocessing (PSP) ，它利用图像之间的感知相似性来减轻在较少标注的场景中的主观偏差。具体来说，我们将主观评分建模为基于先前评分图像的感知相似性的条件概率模型，称为潜意识参考评分。参考图像通过基于感知深度的图像特征的最近邻搜索的标准化矢量点积得到。然后，通过潜意识参考评分的指数移动平均（EMA）对预处理得分进行更新，称为相似正则化EMA。 在多个数据集（LIVE，TID2013，CID2013）上的实验证明，这种方法可以有效地去除主观评分的偏差。此外，实验结果表明，预处理的数据集对于下游IQA任务的性能非常有利。
URL
https://arxiv.org/abs/2404.19666
