# Blind Image Quality Assessment with Deep Learning:
## 1 Introduction  
The exponential growing trend in the amount of digital data generated over the last decade has been accompanied by the increase in attention from multiple research communities (e.g., signal processing, computer vision, and information retrieval, etc.). With respect to the computer vision community, continuous efforts are focused on the development of imaging systems able to capture, process, store, compress, transmit, and display digital images. One specific challenge still to be overcome by these systems relates to the fact that digital images can be affected by a wide range of degradation, also denoted in related literature as distortion, namely noise, JPEG artifacts, aliasing, lens and motion blur, over-sharpening, wrong exposure, color fringing, and over-saturation, among others. This, more often than not, results in a degradation of their visual quality, which affects the remaining building blocks of such architectures [1]. Image Quality Assessment (IQA) is a research area that focuses on the development of computational models that automatically measure the perceptual quality of an image. Such models are needed to evaluate and improve the quality of digital images without human intervention. IQA methods have been presented in the literature in three main categories [2]:  
* Full-reference (FR) to predict quality scores of a target image with full access to the original reference image;* Reduced-reference (RR) to predict quality scores of a target image with limited access to the reference image;
* No-reference (NR) or Blind to predict quality scores of a target image without any information about original reference image. The perceptual quality score is a subjective measure. It is generally defined as the mean of the individual ratings of perceived quality assigned by human subjects, and it is also called Mean Opinion Score (MOS) [3]. Several FR and RR approaches have been proposed in the literature with high performance on the task of assessing image quality [4; 5]. However, when it comes to real-world scenarios, these methods are negatively impacted by the non-availability of the reference images. Without having access to a reference image, developing accurate quality assessment methods becomes more challenging. Current research efforts in Blind IQA (BIQA) methods concentrate on advancing robust solutions for image quality assessment, with applications in areas where having access to a reference image at the time of the assessment is not possible. Recently, deep learning approaches developed for image classification tasks have outperformed traditional methods, which has attracted the attention of researchers in the field of IQA. Consequently, deep learning methods have been developed for BIQA purposes, mainly as a consequence of the ability of Convolutional Neural Networks (CNN) to extract powerful features from images without the need of having a reference image [6; 7]. BIQA algorithms are usually categorized into two classes according to the distortion types [1; 2] they target:  
* Distortion-specific algorithms [8], which use specific distortion models to predict the perceptual quality and measure one or more distortions in an image;
* General-purpose algorithms [9; 10], which measure the perceptual quality of an image across several types of distortions by using the general quality features implied in the image. We have identified general-purpose BIQA methods as having great potential for applications in unconstrained environments, such as lifelogging applications. Lifelogging is the process of tracking and recording personal data created though our activities and behavior [11]. In particular, this personal data, also called lifelogs, can be capture in the form of visual lifelogs such as images or videos [12]. Information retrieval is an important aspect connected to lifelogging since in most lifelogging applications the objective is to accurately retrieve lifelogs that answers to a specific research question.