3D-MAD: A Full Reference Stereoscopic Image Quality Estimator Based on Binocular Lightness and Contrast Perception

Abstract

Algorithms for a stereoscopic image quality assessment (IQA) aim to estimate the qualities of 3D images in a manner that agrees with human judgments. The modern stereoscopic IQA algorithms often apply 2D IQA algorithms on stereoscopic views, disparity maps, and/or cyclopean images, to yield an overall quality estimate based on the properties of the human visual system. This paper presents an extension of our previous 2D most apparent distortion (MAD) algorithm to a 3D version (3D-MAD) to evaluate 3D image quality. The 3D-MAD operates via two main stages, which estimate perceived quality degradation due to 1) distortion of the monocular views and 2) distortion of the cyclopean view. In the first stage, the conventional MAD algorithm is applied on the two monocular views, and then the combined binocular quality is estimated via a weighted sum of the two estimates, where the weights are determined based on a block-based contrast measure. In the second stage, intermediate maps corresponding to the lightness distance and the pixel-based contrast are generated based on a multipathway contrast gain-control model. Then, the cyclopean view quality is estimated by measuring the statistical-difference-based features obtained from the reference stereopair and the distorted stereopair, respectively. Finally, the estimates obtained from the two stages are combined to yield an overall quality score of the stereoscopic image. Tests on various 3D image quality databases demonstrate that our algorithm significantly improves upon many other state-of-the-art 2D/3D IQA algorithms.

Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting

“…where g l and g r are the significant level estimates of the left and right views, respectively, and equations (4) and (5), respectively, represent the weights of the left and right views, thereby obtaining a weighted quality score of the left and right views quality scores:…”

“…With the rapid development of stereo image applications, many related stereo image technologies and services have been introduced in our daily lives as well as in many professional fields [1][2][3][4][5][6][7][8][9]. A variety of distortions can occur during the collection, transmission, processing, and displaying of stereo images [10][11][12][13][14][15][16][17][18][19].…”

“…erefore, objective evaluation plays a dominant role in SIQE. ere are three main types of objective stereo image quality evaluation methods: full-reference (FR) evaluation [4][5][6][7][8], reduced-reference (RR) evaluation [9], and no-reference (NR) evaluation [10][11][12][13][14][15][16][17][18][19][20][21][22]. e FR evaluation method compares the undistorted original image with the distorted image to obtain the difference between them.…”

Sparsity based stereoscopic image quality assessment

“…In this paper we consider only disparity maps of reference stereo pairs and thus avoid the issues in estimating disparity maps of impaired stereo pairs. Zhang et al applied their previous 2D-MAD [5] model to stereo called 3D-MAD [6] in two stages. Firstly they apply on both left and right images separately and their quality scores are linearly combined.…”

A Blind Stereoscopic Image Quality Evaluator With Segmented Stacked Autoencoders Considering the Whole Visual Perception Route

“…Lee et al [11] proposed a model that divides a stereo image into binocular and monocular vision segments and applies different visual weights to the pooling method. Zhang et al [12] devised a 3D-MAD that estimates perceived quality degradation by distortion of monocular views and a cyclopean view respectively.…”