Abstract
Human visual perceptual model is a key factor for evaluating stereoscopic image quality. This paper focuses on the contributions of monocular and binocular properties on quality perception and proposes a novel blind stereoscopic image quality assessment model by comprehensively digging the relationship between visual features and quality perception. The statistical quality-aware monocular features are extracted from both left view and right view to reveal monocular quality perception, including the color statistical features which are missed in most previous models, while the multiple features of the summation signal and the entropy features of the difference signal are extracted to quantify the binocular quality perception. Finally, support vector regression (SVR) is utilized to train a regression model based on the extracted features and the subjective scores. Three public databases, LIVE 3D Phase I, LIVE 3D Phase II, and MCL 3D Database, are adopted to prove the effectiveness of the proposed model. Experimental results demonstrate that the proposed model is superior to other existing state-of-the-art quality metrics.
Highlights
Over recent years, stereoscopic contents have grown explosively, including stereoscopic film, virtual reality, 3D-TV, and so on, which drive the demand and development of stereoscopic image quality assessment (SIQA)
LIVE 3D Phase I contains 365 distorted images with a co-registered human score in the form of the difference mean opinion score (DMOS). They created from 20 reference images and affected symmetrically by five types of distortions: JP2K (80), JPEG(80), white noise (WN) (80), fast fading (FF) (80), and Blur (45)
MCL 3D Database consists of 20 reference images and 648 symmetrically distorted stereoscopic images affected by 6 types of distortions (JPEG, JP2K, GB, WN, down-sampling blur (SB), and transmission error (TE)) at four distortion levels
Summary
Stereoscopic contents have grown explosively, including stereoscopic film, virtual reality, 3D-TV, and so on, which drive the demand and development of stereoscopic image quality assessment (SIQA). Based on the model of human double-channel theory, Yang et al [15] modeled human visual summation and difference channels to extract image features and utilized the SVR method to predict the quality of the stereoscopic images. These models have better performance than previous works, which provide us a way to extract the quality-sensitive visual features to build the SIQA method. Various factors should be considered, including monocular image quality, binocular interaction (binocular rivalry, suppression, etc.), and depth information To cope with this challenge, many works have been proposed.
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