# Deep Activation Pooling for Blind Image Quality Assessment
## 1 Introduction  
Nowadays, there are many applications [1; 2], such as image transmission, acquisition, compression, enhancement and analysis, that require an efficient and accurate algorithm for image quality assessment (IQA). Generally, the IQA methods can be separated into two major classes: subjective assessment and objective assessment. As the final evaluation criterion of an image, the subjective assessment is mainly conducted by the pretrained human observers, which is time-consuming and labour-intensive. Hence, more and more researchers are devoted to the development of objective IQA methods that can automatically evaluate the image quality. One trend is to develop full reference IQA (FR-IQA) algorithms when the pristine reference image is available. For example, [PERSON] et al. [3] utilized the difference of gradients between the source and distorted images to evaluate the image quality. [PERSON] et al. [4] proposed a novel feature similarity (FSIM) index which combines the phase information with image gradient magnitudes for FR-IQA. [PERSON] et al. [5] presented the gradient magnitude similarity deviation (GMSD) and achieved impressive accuracy. However, these FR-IQA methods evaluate image quality by using the pristine reference images, which are usually unavailable. To overcome the above-mentioned limitation, many no-reference IQA (NR-IQA) methods [6; 7; 8; 9] have been proposed. Early NR-IQA methods assume that the assessed images are influenced by one or multiple types of known distortions, for example, ringing, blockiness or compression, and meanwhile, the IQA models are designed for these known distortions. Hence, the application field ofthese approaches is very limited. The blind IQA (BIQA) is a new trend of NR-IQA research, and it does not need to know the distortion types. Most existing BIQA methods are opinion-aware [10; 11; 12; 13], which trains a regression model from numerous training images (source and distortion images) with the homologous human subjective scores. Specifically, the features are first obtained from the training images, and then both feature vectors and corresponding human subjective scores are employed to train a regression model. In the test stage, the feature vector of an image is fed into the trained regression model to generate the quality score. In [12], [PERSON] et al. utilized several perceptually relevant image features for the opinion-aware BIQA task. The features contain the entropy and mean value of phase congruency image, and the entropy and gradient of the distorted image. [PERSON] et al. [14] proposed the blind/referenceless image spatial quality evaluator (BRISQUE) to assess image quality. In [15], [PERSON] and [PERSON] proposed an efficient NR-IQA algorithm using a log-derivative statistical model of natural scenes. [PERSON] et al. [16] combined the normalized gradient magnitude (GM) with the Laplacian of Gaussian (LOG) features to learn a regression model. There are two main drawbacks of opinion-aware BIQA. Firstly, the approach requires a large amount of distorted images to learn the regression model. In practice, however, it is a challenging task to collect enough training images to cover all kinds of distortions. Secondly, since the image distortion types are diverse and there may be several interacting distortions contained in one image, the opinion-aware BIQA models have a weak generalization ability. This means that the image quality scores are often inaccurate when the model learned from one database is directly applied to another one. Recently, many researchers have turned to the attractive and challenging study of the opinion-unaware BIQA methods, which only use the pristine naturalistic images to train a model. The opinion-unaware BIQA methods are independent of the distorted images and relevant subjective quality scores, and therefore they possess more potential to deliver higher generalization capability than the opinion-aware ones.