Quality Index for Stereoscopic Images by Separately Evaluating Adding and Subtracting

The human visual system (HVS) plays an important role in stereo image quality perception. Therefore, it has aroused many people’s interest in how to take advantage of the knowledge of the visual perception in image quality assessment models. This paper proposes a full-reference metric for quality assessment of stereoscopic images based on the binocular difference channel and binocular summation channel. For a stereo pair, the binocular summation map and binocular difference map are computed first by adding and subtracting the left image and right image. Then the binocular summation is decoupled into two parts, namely additive impairments and detail losses. The quality of binocular summation is obtained as the adaptive combination of the quality of detail losses and additive impairments. The quality of binocular summation is computed by using the Contrast Sensitivity Function (CSF) and weighted multi-scale (MS-SSIM). Finally, the quality of binocular summation and binocular difference is integrated into an overall quality index. The experimental results indicate that compared with existing metrics, the proposed metric is highly consistent with the subjective quality assessment and is a robust measure. The result have also indirectly proved hypothesis of the existence of binocular summation and binocular difference channels

Objective Evaluation Criteria for Shooting Quality of Stereo Cameras over Short Distance

Stereo cameras are the basic tools used to obtain stereoscopic image pairs, which can lead to truly great image quality. However, some inappropriate shooting conditions may cause discomfort while viewing stereo images. It is therefore considerably necessary to establish the perceptual criteria that can be used to evaluate the shooting quality of stereo cameras. This article proposes objective quality evaluation criteria based on the characteristics of parallel and toed-in camera configurations. Considering the different internal structures and basic shooting principles, this paper focuses on short-distance shooting conditions and establishes assessment criteria for both parallel and toed-in camera configurations. Experimental results show that the proposed evaluation criteria can predict the visual perception of stereoscopic images and effectively evaluate stereoscopic image quality

Quality index for stereoscopic images by jointly evaluating cyclopean amplitude and cyclopean phase

With widespread applications of three-dimensional (3-D) technology, measuring quality of experience for 3-D multimedia content plays an increasingly important role. In this paper, we propose a full reference stereo image quality assessment (SIQA) framework which focuses on the innovation of binocular visual properties and applications of low-level features. On one hand, based on the fact that human visual system understands an image mainly according to its low-level features, local phase and local amplitude extracted from phase congruency measurement are employed as primary features. Considering the less prominent performance of amplitude in IQA, visual saliency is applied into the modification on amplitude. On the other hand, by fully considering binocular rivalry phenomena, we create the cyclopean amplitude map and cyclopean phase map. With this method, both image features and binocular visual properties are mutually combined with each other. Meanwhile, a novel binocular modulation function in spatial domain is also adopted into the overall quality prediction of amplitude and phase. Extensive experiments demonstrate that the proposed framework achieves higher consistency with subjective tests than relevant SIQA metrics

Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory

Objective quality assessment of stereoscopic omnidirectional images is a challenging problem since it is influenced by multiple aspects such as projection deformation, field of view (FoV) range, binocular vision, visual comfort, etc. Existing studies show that classic 2D or 3D image quality assessment (IQA) metrics are not able to perform well for stereoscopic omnidirectional images. However, very few research works have focused on evaluating the perceptual visual quality of omnidirectional images, especially for stereoscopic omnidirectional images. In this paper, based on the predictive coding theory of the human vision system (HVS), we propose a stereoscopic omnidirectional image quality evaluator (SOIQE) to cope with the characteristics of 3D 360-degree images. Two modules are involved in SOIQE: predictive coding theory based binocular rivalry module and multi-view fusion module. In the binocular rivalry module, we introduce predictive coding theory to simulate the competition between high-level patterns and calculate the similarity and rivalry dominance to obtain the quality scores of viewport images. Moreover, we develop the multi-view fusion module to aggregate the quality scores of viewport images with the help of both content weight and location weight. The proposed SOIQE is a parametric model without necessary of regression learning, which ensures its interpretability and generalization performance. Experimental results on our published stereoscopic omnidirectional image quality assessment database (SOLID) demonstrate that our proposed SOIQE method outperforms state-of-the-art metrics. Furthermore, we also verify the effectiveness of each proposed module on both public stereoscopic image datasets and panoramic image datasets

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

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

Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

Objective quality assessment of stereoscopic panoramic images becomes a challenging problem owing to the rapid growth of 360-degree contents. Different from traditional 2D image quality assessment (IQA), more complex aspects are involved in 3D omnidirectional IQA, especially unlimited field of view (FoV) and extra depth perception, which brings difficulty to evaluate the quality of experience (QoE) of 3D omnidirectional images. In this paper, we propose a multi-viewport based fullreference stereo 360 IQA model. Due to the freely changeable viewports when browsing in the head-mounted display (HMD), our proposed approach processes the image inside FoV rather than the projected one such as equirectangular projection (ERP). In addition, since overall QoE depends on both image quality and depth perception, we utilize the features estimated by the difference map between left and right views which can reflect disparity. The depth perception features along with binocular image qualities are employed to further predict the overall QoE of 3D 360 images. The experimental results on our public Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) show that the proposed method achieves a significant improvement over some well-known IQA metrics and can accurately reflect the overall QoE of perceived images

Stereoscopic image quality assessment method based on binocular combination saliency model

The objective quality assessment of stereoscopic images plays an important role in three-dimensional (3D) technologies. In this paper, we propose an effective method to evaluate the quality of stereoscopic images that are afflicted by symmetric distortions. The major technical contribution of this paper is that the binocular combination behaviours and human 3D visual saliency characteristics are both considered. In particular, a new 3D saliency map is developed, which not only greatly reduces the computational complexity by avoiding calculation of the depth information, but also assigns appropriate weights to the image contents. Experimental results indicate that the proposed metric not only significantly outperforms conventional 2D quality metrics, but also achieves higher performance than the existing 3D quality assessment models

Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement

Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3D contents. Compared with conventional methods which are relied on hand-crafted features, deep learning oriented measurements have achieved remarkable performance in recent years. However, most existing deep SIQM evaluators are not specifically built for stereoscopic contents and consider little prior domain knowledge of the 3D human visual system (HVS) in network design. In this paper, we develop a Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality measurement. In the first stage, inspired by the predictive coding theory that the cognition system tries to match bottom-up visual signal with top-down predictions, we adopt the encoder-decoder architecture to reconstruct the distorted inputs. Besides, motivated by the binocular rivalry phenomenon, we leverage the likelihood and prior maps generated from the predictive coding process in the Siamese framework for assisting SIQM. In the second stage, quality regression network is applied to the fusion image for acquiring the perceptual quality prediction. The performance of PAD-Net has been extensively evaluated on three benchmark databases and the superiority has been well validated on both symmetrically and asymmetrically distorted stereoscopic images under various distortion types