Blind image quality assessment (BIQA) remains a very challenging problem due
to the unavailability of a reference image. Deep learning based BIQA methods
have been attracting increasing attention in recent years, yet it remains a
difficult task to train a robust deep BIQA model because of the very limited
number of training samples with human subjective scores. Most existing methods
learn a regression network to minimize the prediction error of a scalar image
quality score. However, such a scheme ignores the fact that an image will
receive divergent subjective scores from different subjects, which cannot be
adequately represented by a single scalar number. This is particularly true on
complex, real-world distorted images. Moreover, images may broadly differ in
their distributions of assigned subjective scores. Recognizing this, we propose
a new representation of perceptual image quality, called probabilistic quality
representation (PQR), to describe the image subjective score distribution,
whereby a more robust loss function can be employed to train a deep BIQA model.
The proposed PQR method is shown to not only speed up the convergence of deep
model training, but to also greatly improve the achievable level of quality
prediction accuracy relative to scalar quality score regression methods. The
source code is available at this https URL

提出了一种新的概率图像质量表示方法（PQR），用于训练深度 BIQA 模型，该方法不仅可以加速深度模型训练的收敛，而且相对于标量质量得分回归方法，也可以极大地提高可实现的质量预测精度。