computer-vision-1

Stereoscopic image quality assessment

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

AdaBoost neural network and cyclopean view for no-reference stereoscopic image quality assessment

Oussama Messai, F. Hachouf, Z. A. Seghir•Sat Feb 29 2020

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.

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No-reference Stereoscopic Image Quality Predictor using Deep Features from Cyclopean Image

Oussama Messai, F. Hachouf, Z. A. Seghir, A. Chetouani•Sun Jan 17 2021

A blind stereoscopic image quality measurement using synthesized cyclopean image and deep feature extraction based on Human Visual System (HVS) modeling and quality-aware indicators is introduced.

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A Multi-Task Convolutional Neural Network For Blind Stereoscopic Image Quality Assessment Using Naturalness Analysis

Salima Bourbia, Ayoub Karine, A. Chetouani, Mohammed El Hassouni Lrit, Mohammed V Rabat, Rabat, Morocco., Isen Yncrea Ouest, 33 Quater Chemin du Champ de Manoeuvre, 44470 Carquefou, France, Laboratoire Prisme, Universit'e d'Orl'eans, Flsh•Wed Jun 16 2021

This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method and compute naturalness-based features using a Natural Scene Statistics (NSS) model in the complex wavelet domain.

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End-to-End Deep Multi-Score Model for No-Reference Stereoscopic Image Quality Assessment

Oussama Messai, A. Chetouani•Sat Oct 15 2022

A deep multi-score Convolutional Neural Network that has been trained to perform four tasks: predict the left view’s quality, predict the quality of the stereo view and global quality, respectively, with the global score serving as the ultimate quality.

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3D saliency guided deep quality predictor for no-reference stereoscopic images

Oussama Messai, F. Hachouf, Z. A. Seghir, A. Chetouani•Fri Dec 31 2021

A new deep learning-based no-reference SIQA using cyclopean view hypothesis and human visual attention is introduced using cyclopean view hypothesis and human visual attention.

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