Predictive Sampling for Efficient Pairwise Subjective Image Quality Assessment
MM '23: Proceedings of the 31st ACM International Conference on Multimedia(2023)
摘要
Subjective image quality assessment studies are used in many scenarios, such
as the evaluation of compression, super-resolution, and denoising solutions.
Among the available subjective test methodologies, pair comparison is
attracting popularity due to its simplicity, reliability, and robustness to
changes in the test conditions, e.g. display resolutions. The main problem that
impairs its wide acceptance is that the number of pairs to compare by subjects
grows quadratically with the number of stimuli that must be considered.
Usually, the paired comparison data obtained is fed into an aggregation model
to obtain a final score for each degraded image and thus, not every comparison
contributes equally to the final quality score. In the past years, several
solutions that sample pairs (from all possible combinations) have been
proposed, from random sampling to active sampling based on the past subjects'
decisions. This paper introduces a novel sampling solution called
Predictive Sampling for Pairwise Comparison
(PS-PC) which exploits the characteristics of the input data to make a
prediction of which pairs should be evaluated by subjects. The proposed
solution exploits popular machine learning techniques to select the most
informative pairs for subjects to evaluate, while for the other remaining
pairs, it predicts the subjects' preferences. The experimental results show
that PS-PC is the best choice among the available sampling algorithms with
higher performance for the same number of pairs. Moreover, since the choice of
the pairs is done a priori before the subjective test starts, the
algorithm is not required to run during the test and thus much more simple to
deploy in online crowdsourcing subjective tests.
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关键词
quality,assessment,pairwise
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