No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Peng Xu,Lei Chen,Qingshan Chen,Man Guo,Weifeng Hu,Yujun Li,Jia Wu

doi:10.1155/2021/8834652

Abstract

Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.

Highlights

During the past few decades, there has been an exponential increase of stereoscopic images and videos in 3D display market [1]
In our previous work [34], we developed a no-reference quality prediction scheme for 3D images based on binocular features and support vector regression (SVR). e scheme showed its effectiveness, but the performance in terms of prediction accuracy and time complexity needs to be further improved
We primarily work on extracting certain types of binocular features from distorted stereoscopic image and constructing a statistical regression model to map these quality-aware features to the human perceptual judgements. e main contributions of this work are as follows: (1) Different from other related studies [33, 35], the novelty of our work lies in that we propose to adopt the effective binocular statistical features from the fusion and difference maps of a stereopair for stereoscopic image quality prediction

Summary

Introduction

During the past few decades, there has been an exponential increase of stereoscopic images and videos in 3D display market [1]. Due to various 3D quality factors [2, 3] including binocular rivalry, visual comfort, and depth perception, the visual quality assessment of stereoscopic images is much more complex and relatively less researched than the traditional 2D image quality evaluation. To address these challenges, we require a deeper understanding of binocular vision mechanisms and interactions for the quality prediction of distorted stereoscopic images. It is in urgent demand to propose objective methods that can effectively evaluate the human perceptual quality of stereoscopic images

Objectives

Results

Conclusion