You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper, we present a novel method of no-reference image quality assessment (NR-IQA), which is to predict the perceptual quality score of a given image without using any reference image. The proposed method harnesses three functions (i) the visual attention mechanism, which affects many aspects of visual perception including image quality assessment, however, is overlooked in the NR-IQA literature. The method assumes that the fixation areas on an image contain key information to the process of IQA. (ii) the robust averaging strategy, which is a means --- supported by psychology studies --- to integrating multiple step-wise evidence to make a final perceptual judgment. (iii) the multi-task learning, which is believed to be an effectual means to shape representation learning and could result in a more generalized model. To exploit the synergy of the three, we consider the NR-IQA as a dynamic perception process, in which the model samples a sequence of "informative" areas and aggregates the information to learn a representation for the tasks of jointly predicting the image quality score and the distortion type. The model learning is implemented by a reinforcement strategy, in which the rewards of both tasks guide the learning of the optimal sampling policy to acquire the "task-informative" image regions so that the predictions can be made accurately and efficiently (in terms of the sampling steps). The reinforcement learning is realized by a deep network with the policy gradient method and trained through back-propagation. In experiments, the model is tested on the TID2008 dataset and it outperforms several state-of-the-art methods. Furthermore, the model is very efficient in the sense that a small number of fixations are used in NR-IQA.

Cited Abstract(s):
[1]: Nowadays, Deep Neural Networks have been applied to many applications (such as classification, denoising and inpainting) and achieved impressive performance. However, most of these works pay much attention to describe how to construct the relative framework but ignore to provide a clear and intuitive understanding of why their framework performs so well. In this paper, we present a general-purpose no-reference (NR) image quality assessment (IQA) framework based on deep neural network and give insight into the operation of this network. In this NR-IQA framework, simple features are extracted by a new multiscale directional transform (shearlet transform) and the sum of subband coefficient amplitudes (SSCA) is utilized as primary features to describe the behavior of natural images and distorted images. Then, stacked autoencoders are applied as 'evolution process' to 'amplify' the primary features and make them more discriminative. Finally, by translating the NR-IQA problem into classification problem, the differences of evolved features are identified by softmax classifier. Moreover, we have also incorporated some visualization techniques to analysis and visualize this NR-IQA framework. The resulting algorithm, which we name SESANIA (ShEarlet and Stacked Autoencoders based No-reference Image quality Assessment) is tested on several database (LIVE, Multiply Distorted LIVE and TID2008) individually and combined together. Experimental results demonstrate the excellent performance of SESANIA, and we also give intuitive explanations of how it works and why it works well. In addition, SESANIA is extended to estimate quality in local regions. Further experiments demonstrate the local quality estimation ability of SESANIA on images with local distortions. A NR-IQA framework is proposed using shearlet transform and deep neural networks.Intuitive explanations of how this framework works and why it works well are given.This framework is extended to estimate image quality in local regions.

[2]: 

[3]: It is often desirable to evaluate an image based on its quality. For many computer vision applications, a perceptually meaningful measure is the most relevant for evaluation; however, most commonly used measure do not map well to human judgements of image quality. A further complication of many existing image measure is that they require a reference image, which is often not available in practice. In this paper, we present a “blind” image quality measure, where potentially neither the groundtruth image nor the degradation process are known. Our method uses a set of novel low-level image features in a machine learning framework to learn a mapping from these features to subjective image quality scores. The image quality features stem from natural image measure and texture statistics. Experiments on a standard image quality benchmark dataset shows that our method outperforms the current state of art.
In addition to the patch-based methods, some methods combine hand-crafted low-level features with deep networks as an alternative approach. For example, Tang al [1]5 first extract the LBIQ features [1]3 and feed the features into a Restricted Boilzman Machine to predict image quality scores. Hou al [1] pose IQA problem as a classification problem. They slot images into different categories according to the image quality and propose a quality pooling method under the Bayesian framework to predict quality scores.