Adaboost Neural Network And Cyclopean View For No-reference Stereoscopic Image Quality Assessment

Paper title: "Adaboost Neural Network And Cyclopean View For No-reference Stereoscopic Image Quality Assessment" This code is the implementation of the proposed method called 3D-AdaBoost.

Abtract :

Stereoscopic imaging has been widely used in many fields. In many scenarios, stereo images quality could be affected by various degradations, such as asymmetric distortion. Accordingly, to guarantee the best quality of experience, robust and accurate reference-less metrics are required for quality assessment of stereoscopic content. Most existing stereo no-reference Image Quality Assessment (IQA) models are not consistent with asymmetrical distortions. This paper presents a new no-reference stereoscopic image quality assessment metric using a human visual system (HVS) modeling and an advanced machine-learning algorithm. The proposed approach consists of two stages. In the first stage, cyclopean image is constructed considering the presence of binocular rivalry in order to cover the asymmetrically distorted part. In the second stage, gradient magnitude, relative gradient magnitude, and gradient orientation are extracted. These are used as a predictive source of information for the quality. In order to obtain the best overall performance against different databases, Adaptive Boosting (AdaBoost) idea of machine learning combined with artificial neural network model has been adopted. The benchmark LIVE 3D phase-I, phase-II, and IRCCyN/IVC 3D databases have been used to evaluate the performance of the proposed approach. Experimental results have demonstrated that the proposed metric performance achieves high consistency with subjective assessment and outperforms the blind stereo IQA over various types of distortion.

Publisher URL: https://www.sciencedirect.com/science/article/pii/S0923596519300736

DOI: 10.1016/j.image.2019.115772

IQA datasets:

We have reported experimental results on different IQA datasets including LIVE 3D Phase I, LIVE 3D Phase II, and ICV 3D.

Citation :

If you use this code, we kindly ask you to cite the paper :

Messai O, Hachouf F, Seghir ZA. AdaBoost neural network and cyclopean view for no-reference stereoscopic image quality assessment. 
Signal Processing: Image Communication. 2020 Mar 1;82:115772.

BibTex :

@article{messai2020adaboost,
  title={AdaBoost neural network and cyclopean view for no-reference stereoscopic image quality assessment},
  author={Messai, Oussama and Hachouf, Fella and Seghir, Zianou Ahmed},
  journal={Signal Processing: Image Communication},
  volume={82},
  pages={115772},
  year={2020},
  publisher={Elsevier}
}