CoDIQE3D: A completely blind, no-reference stereoscopic image quality estimator using joint color and depth statistics

Abstract

In this paper, we present an unsupervised, completely blind, no-reference (NR) stereoscopic (S3D) image quality prediction model to assess the perceptual quality of natural S3D images. We study the joint dependencies between color and depth features of S3D images and empirically model these dependencies by using a bivariate generalized Gaussian distribution (BGGD). We compute the parameters of BGGD, and we also obtain the determinant and the coherence values from the covariance matrix of the proposed BGGD model. We extract the features of BGGD model and covariance matrix from the reference S3D image, followed by multivariate Gaussian (MVG) distribution modeling on the predicted features of the reference. We estimate the joint color and depth quality of the S3D images by computing the likelihood of the image features with respect to the reference MVG model. We apply the popular 2D unsupervised NIQE model on individual stereo views to estimate the overall spatial quality of the S3D images. Finally, we pool the likelihood scores and the spatial NIQE scores to achieve the estimation for the overall perceived quality of the S3D images. The performance of the proposed model is evaluated on the MICT, LIVE Phase I and II S3D image datasets. The results indicate consistent and robust performance for all datasets. Our proposed estimator is completely blind, as it requires neither training on subjective scores nor reference S3D images. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.