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Due to the subjective nature of image quality assessment (IQA), assessingwhich image has better quality among a sequence of images is more reliable thanassigning an absolute mean opinion score for an image. Thus, IQA models areevaluated 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 tothe infeasible computation of GCC metrics during training. In this work, weconstruct a novel loss function and network to exploit Global-correlation andMean-opinion Consistency, forming a GMC-IQA framework. Specifically, we proposea novel GCC loss by defining a pairwise preference-based rank estimation tosolve the non-differentiable problem of SROCC and introducing a queue mechanismto reserve previous data to approximate the global results of the whole data.Moreover, we propose a mean-opinion network, which integrates diverse opinionfeatures to alleviate the randomness of weight learning and enhance the modelrobustness. Experiments indicate that our method outperforms SOTA methods onmultiple authentic datasets with higher accuracy and generalization. We alsoadapt the proposed loss to various networks, which brings better performanceand more stable training.

GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment