Blind image quality assessment is a challenging task particularly due to the
unavailability of reference information. Training a deep neural network
requires a large amount of training data which is not readily available for
image quality. Transfer learning is usually opted to overcome this limitation
and different deep architectures are used for this purpose as they learn
features differently. After extensive experiments, we have designed a deep
architecture containing two CNN architectures as its sub-units. Moreover, a
self-collected image database BIQ2021 is proposed with 12,000 images having
natural distortions. The self-collected database is subjectively scored and is
used for model training and validation. It is demonstrated that synthetic
distortion databases cannot provide generalization beyond the distortion types
used in the database and they are not ideal candidates for general-purpose
image quality assessment. Moreover, a large-scale database of 18.75 million
images with synthetic distortions is used to pretrain the model and then
retrain it on benchmark databases for evaluation. Experiments are conducted on
six benchmark databases three of which are synthetic distortion databases
(LIVE, CSIQ and TID2013) and three are natural distortion databases (LIVE
Challenge Database, CID2013 and KonIQ-10 k). The proposed approach has provided
a Pearson correlation coefficient of 0.8992, 0.8472 and 0.9452 subsequently and
Spearman correlation coefficient of 0.8863, 0.8408 and 0.9421. Moreover, the
performance is demonstrated using perceptually weighted rank correlation to
indicate the perceptual superiority of the proposed approach. Multiple
experiments are conducted to validate the generalization performance of the
proposed model by training on different subsets of the databases and validating
on the test subset of BIQ2021 database.

提出了一种基于深度学习的方法，包含两个 CNN 子单元的深度架构，以及一个自行收集的包含自然扭曲的图像的图像库 BIQ2021，用于模型训练和验证。通过在多个数据集上的实验，证明了该方法的性能和广泛的泛化性能。