# Multi-Pooled Inception Features for No-Reference Image Quality Assessment
## 1 Introduction
### Related Work  
Many traditional NR-IQA algorithms rely on the so-called natural scene statistics (NSS) [1] model. These methods assume that natural images possess a particular regularity that is modified by visual distortion. Further, by quantifying the deviation from the natural statistics, perceptual image quality can be determined. NSS-based feature vectors usually rely on the wavelet transform [2], discrete cosine transform [3], curvelet transform [4], shearlet transform [5], or transforms to other spatial domains [6]. DIIVINE [2] (Distortion Identification-based Image Verity and INtegrity Evaluation) exploits NSS using wavelet transform and consists of two steps. Namely, a probabilistic distortion identification stage is followed by a distortion-specific quality assessment one. In contrast, [PERSON] et al. [7] presented a sparse feature representation of NSS using also the wavelet transform. [PERSON] et al. [3] built a feature vector from DCT coefficients. Subsequently, a Bayesian inference approach was applied for the prediction of perceptual quality scores. In [8], the authors presented a detailed review about the use of local binary pattern texture descriptors in NR-IQA. Another line of work focuses on opinion-unaware algorithms that require neither training samples nor human subjective scores. [PERSON] et al. [9] introduced the integrated local natural image quality evaluator (IL-NIQE), which combines features of NSS with multivariate Gaussian models of image patches. This evaluator uses several quality-aware NSS features, i.e., the statistics of normalized luminance, mean subtracted and contrast-normalized products of pairs of adjacent coefficients, gradient, log-Gabor filter responses, and color (after the transformation into a logarithmic-scale opponent color space). [PERSON] et al. [10] introduced a no-reference image quality predictor called the blind image evaluator based on a convolutional neural network (BIECON), in which the training process is carried out in two steps. First, local metric score regression and then subjective score regression are conducted. During the local metric score regression, non-overlapping image patches are trained independently; FR-IQA methods such as SSIM or GMS are used for the target patches. Then, the CNN trained on image patches is refined by targeting the subjective image score of the complete image. Similarly, the training of a multi-task end-to-end optimized deep neural network [11] is carried out in two steps. Namely, this architecture contains two sub-networks: a distortion identification network and a quality prediction network. Furthermore, a biologically inspired generalized divisive normalization [12] is applied as the activation function in the network instead of rectified linear units (ReLUs). Similarly, [PERSON] et al. [13] introduced a two-stage framework. First, a distortion type classifier identifies the distortion type then a fusion algorithm is applied to aggregate the results of expert networks and produce a perceptual quality score. In recent years, many algorithms relying on deep learning have been proposed. Because of the small size of many existing image quality benchmark databases, most deep learning based methods employ CNNs as feature extractors or take patches from the training images to increase the database size. The CNN framework of [PERSON] et al. [14] is trained on non-overlapping image patches extracted from the training images. Furthermore, these patches inherit the MOS of their source images. For preprocessing, local contrast normalization is employed. The applied CNN consists of conventional building blocks, such as convolutional, pooling, and fully connected layers. [PERSON] et al. [15] introduced a similar method. Namely, they developed a 12-layer CNN that is trained on \(32\times 32\) image patches. Furthermore, a weighted average patch aggregation method was introducedin which weights representing the relative importance of image patches in quality assessment are learned by a subnetwork. In contrast, [PERSON] et al.