GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for
  No-reference Image Quality Assessment
Zewen Chen Juan Wang Bing Li Chunfeng Yuan Weiming Hu Junxian Liu Peng Li Yan Wang Youqun Zhang Congxuan Zhang
Due to the subjective nature of image quality assessment (IQA), assessing
which image has better quality among a sequence of images is more reliable than
assigning an absolute mean opinion score for an image. Thus, IQA models are
evaluated by global correlation consistency (GCC) metrics like PLCC and SROCC,
rather than mean opinion consistency (MOC) metrics like MAE and MSE. However,
most existing methods adopt MOC metrics to define their loss functions, due to
the infeasible computation of GCC metrics during training. In this work, we
construct a novel loss function and network to exploit Global-correlation and
Mean-opinion Consistency, forming a GMC-IQA framework. Specifically, we propose
a novel GCC loss by defining a pairwise preference-based rank estimation to
solve the non-differentiable problem of SROCC and introducing a queue mechanism
to reserve previous data to approximate the global results of the whole data.
Moreover, we propose a mean-opinion network, which integrates diverse opinion
features to alleviate the randomness of weight learning and enhance the model
robustness. Experiments indicate that our method outperforms SOTA methods on
multiple authentic datasets with higher accuracy and generalization. We also
adapt the proposed loss to various networks, which brings better performance
and more stable training.