McmIQA: Multi-Module Collaborative Model for No-Reference Image Quality Assessment

No reference image quality assessment is a technique that uses computers to simulate the human visual system and automatically evaluate the perceived quality of images. In recent years, with the widespread success of deep learning in the field of computer vision, many end-to-end image quality assessment algorithms based on deep learning have emerged. However, unlike other computer vision tasks that focus on image content, an excellent image quality assessment model should simultaneously consider distortions in the image and comprehensively evaluate their relationships. Motivated by this, we propose a Multi-module Collaborative Model for Image Quality Assessment (McmIQA). The image quality assessment is divided into three subtasks: distortion perception, content recognition, and correlation mapping. And specific modules are constructed for each subtask: the distortion perception module, the content recognition module, and the correlation mapping module. Specifically, we apply two contrastive learning frameworks on two constructed datasets to train the distortion perception module and the content recognition module to extract two types of features from the image. Subsequently, using these extracted features as input, we employ a ranking loss to train the correlation mapping module to predict image quality on image quality assessment datasets. Extensive experiments conducted on seven relevant datasets demonstrated that the proposed method achieves state-of-the-art performance in both synthetic distortion and natural distortion image quality assessment tasks

No-reference image quality assessment based on automatic machine learning

In different applications in deep learning, due to different required features, it is necessary to design specialized Neural Network structure. However, the design of the structure largely depends on the relevant subject knowledge of researchers and lots of experiments, resulting in huge waste of manpower. Therefore, in the field of Image Quality Assessment (IQA), the authors propose a method to apply Neural Architecture Search (NAS) to IQA. Mainly through the Differentiable Architecture Search algorithm, the structure of the modular Neural Network unit is searched by the stochastic gradient descent algorithm with better training performance by relaxing the operation features into a continuous space. Also, the idea of weight sharing is used to further save. The authors use the mainstream IQA database LIVE to search for Neural Network structures, and retrain and validate the searched structures in four datasets. A large number of experiments show that the model obtained by the search experiment achieves the effect of the best algorithm at this stage, and has a certain quality. The main contributions of this paper are: Transform the DARTS algorithm to adapt the regression problem, and introduce the Neural Architecture Search algorithm into the IQA field and conduct experimental verification

Quaternion wavelet transform based full reference image quality assessment for multiply distorted images.

Most of real-world image distortions are multiply distortion rather than single distortion. To address this issue, in this paper we propose a quaternion wavelet transform (QWT) based full reference image quality assessment (FR IQA) metric for multiply distorted images, which jointly considers the local similarity of phase and magnitude of each subband via QWT. Firstly, the reference images and distorted images are decomposed by QWT, and then the similarity of amplitude and phase are calculated on each subband, thirdly the IQA metric is constructed by the weighting method considering human visual system (HVS) characteristics, and lastly the scores of each subband are averaged to get the quality score of test image. Experimental results show that the proposed method outperforms the state of art in multiply distorted IQA

Several IQA comparison on the MDID2013 image database.

<p>Several IQA comparison on the MDID2013 image database.</p

Several IQA comparison on single distortion LIVE image database.

<p>Several IQA comparison on single distortion LIVE image database.</p

Scatter plots of several FR IQA algorithms on the MDID2013 image database.

<p>Scatter plots of several FR IQA algorithms on the MDID2013 image database.</p

Several IQA algorithm comparison on the blur and noise image dataset.

<p>Several IQA algorithm comparison on the blur and noise image dataset.</p

Several IQA algorithm comparison on the blur and JPEG image dataset.

<p>Several IQA algorithm comparison on the blur and JPEG image dataset.</p

Amplitude and phase images of each subband via QWT.

<p>Amplitude and phase images of each subband via QWT.</p