Abstract
Reduced-reference stereoscopic image quality assessment (RRSIQA) models evaluate stereoscopic image quality degradation with partial information about the “ideal-quality” reference stereopair. On one hand, sparse representation in recent theoretical studies of visual cognition has been proved to resemble the strategy used to represent natural images in the primary visual cortex. On the other hand, the joint statistics of gradient magnitude (GM) and Laplacian of Gaussian (LOG) features are popularly utilized to form image semantic structures. Motivated by these findings, we present a new RRSIQA metric using gradient sparse representation and structural degradation in this paper. Concretely, the proposed metric is based on two main tasks: the first task extracts the distribution statistics of visual primitives by gradient sparse representation, while the second task measures structural degradation of stereoscopic image due to the presence of distortion by extracting the joint statistics of GM and LOG features. The former, so-called the binocular perceptual visual information (PVI), aims to effectively integrates the gradient map that is sparser than the image itself. Especially, the process of binocular fusion is simulated by using the mutual information of the gradient-based visual primitives between left and right view’s images as binocular cue. Furthermore, the perceptual loss vectors are taken as the differences of binocular perceptual visual information and structural degradation between reference and distorted stereopairs. Finally, the perceptual loss vectors are utilized to calculate the quality score by a prediction function which is trained using kernel ridge regressing (KRR). The experiments are performed on the popular LIVE 3D IQA databases and Waterloo IVC 3D databases, and experimental results show highly competitive performance with the state-of-the-art algorithms. Moreover, in some challenging cases with particular asymmetric distortion types, the proposed metric can achieves the best quality prediction accuracy in LIVE 3D phase II and Waterloo IVC 3D Phase II.
Highlights
D URING the past two decades, various threedimensional (3D) technologies have advanced rapidly and drastically changed the way people viewed their world
Since human visual system (HVS) is very sensitive to the structural degradation of natural images, this paper considers the joint statistics of gradient magnitude (GM) and Laplacian of Gaussian (LOG) features to measure the structural degradation of each view image of the distorted stereopair, which is a supplement to the monocular cue entropy of gradient primitives (EGP)
(1) We propose a new reference stereoscopic image quality assessment (RRSIQA) model based on two complementary components: the sparsity properties of HVS and the joint statistics of image semantic structural degradation
Summary
D URING the past two decades, various threedimensional (3D) technologies (such as 3D image coding, reconstruction, enhancement, and monitoring, etc.) have advanced rapidly and drastically changed the way people viewed their world. J. Ma et al.: Reduced-Reference SIQA Using Gradient Sparse Representation and Structural Degradation which may give rise to a degradation of 3D visual quality. Ma et al.: Reduced-Reference SIQA Using Gradient Sparse Representation and Structural Degradation which may give rise to a degradation of 3D visual quality For this reason, it is an urgent demand to establish an effective 3D content quality evaluation method. The most direct and reliable method to estimate image quality is by subjective assessment. The subjective metrics are regarded as inconvenient, expensive, and time consuming [1]. These drawbacks provide the motivation for developing efficient and fast objective stereoscopic image quality assessment (SIQA) metrics
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