Sparse representation based stereoscopic image quality assessment accounting for perceptual cognitive process

Abstract

In this paper, we propose a sparse representation based Reduced-Reference Image Quality Assessment (RR-IQA) index for stereoscopic images from the following two perspectives: 1) Human visual system (HVS) always tries to infer the meaningful information and reduces uncertainty from the visual stimuli, and the entropy of primitive (EoP) can well describe this visual cognitive progress when perceiving natural images. 2) Ocular dominance (also known as binocularity) which represents the interaction between two eyes is quantified by the sparse representation coefficients. Inspired by previous research, the perception and understanding of an image is considered as an active inference process determined by the level of "surprise", which can be described by EoP. Therefore, the primitives learnt from natural images can be utilized to evaluate the visual information by computing entropy. Meanwhile, considering the binocularity in stereo image quality assessment, a feasible way is proposed to characterize this binocular process according to the sparse representation coefficients of each view. Experimental results on LIVE 3D image databases and MCL database further demonstrate that the proposed algorithm achieves high consistency with subjective evaluation.

Blind Stereoscopic Image Quality Assessment Accounting for Human Monocular Visual Properties and Binocular Interactions

“…Thus, we obtain the gradient-related information of the cyclopean signal to get the binocular features. The difference signal is given by subtracting the right image from the left image, which has been proved that the difference information can well explain disparity perception in SIQA area [15], [57]. Works [58], [69] revealed that the difference signal is sensitive to the disparity and carries information critical for stereo perception.…”

A full reference quality assessment method with fused monocular and binocular features for stereo images

“… Yang et al (2018) proposed a RR-SIQA method based on sparse coding. Wan, Gu & Zhao (2019) proposed a RR-SIQA method that uses sparse representation and natural scene statistics to simulate the brain's visual perception.…”

Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation

“…With advancements in mathematics, sparse representation methods span a wide variety of applications, especially in the field of image processing, such as image segmentation [11], image denoising [12], visual tracking [13], and image super-resolution [14], etc. Meanwhile, sparse representation also shows great potential in dealing with the IQA issues [15][16][17]. Almost all existing sparse representation-based IQA methods follow a three-stage framework: dictionary learning (DL), qualityaware feature extraction, and regression model learning from subjective opinions.…”