  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.
