No-reference stereoscopic image quality assessment based on hue summation–difference mapping image and binocular joint mutual filtering

Abstract

The no-reference (NR) quality assessment for stereoscopic images plays a significant role in 3D technology, but it also faces great challenges. In this paper, a novel NR stereo image quality assessment (SIQA) method is proposed. Based on the human visual system, this method mimics the summation and difference channels, which consider the binocular interactive perception property, to process the visual information. Especially, the summation and difference images are calculated by the contrast of hue and luminance in color patches. Meanwhile, considering the interactive filtering between the left and right viewpoints, this method uses the filtered information as the weighting factor to integrate the visual information of the summation and difference channels to form the summation-difference mapping image (SDMI). Then, energy entropy, bivariate generalized Gaussian distribution for the joint distribution of SDMI and the depth map subband coefficients, and the local log-Euclidean multivariate Gaussian descriptor are detected as the feature descriptors. Support vector regression, trained by the features, is utilized to predict the quality of stereoscopic images. Experimental results demonstrate that the proposed algorithm achieves high consistency with subjective assessment on four SIQA databases.

Client-Oriented Blind Quality Metric for High Dynamic Range Stereoscopic Omnidirectional Vision Systems

“…Four quality factor pairs are set for compressing each view of HSOI with the JPEG XT, and the corresponding distortion level is represented by L1, L2, L3 and L4, respectively, from high to low. The four quality factor pairs (q, Q) are set to (16,9), (30,19), (50,30) and (90, 72), which correspond to L1, L2, L3 and L4, respectively. Considering the stereoscopic perception of HSOI, the left view or right view is, respectively, compressed with one of the four quality factor pairs to produce nine distortion levels, including four symmetric encoding distortion levels and five asymmetric encoding distortion as shown in Table 5.…”

“…(1) Joint image filtering Previous studies [50] showed that the content difference between the left and right views of a stereoscopic image was due to the existence of parallax, but the final result of human binocular perception undergoing three fluctuations is to form a stable stereoscopic image. Therefore, it can be inferred that there is an interactive filtering effect between the left and right views.…”

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.…”

“…We compare the proposed model with three 2D IQA models (SSIM, BRISQUE [62] and ADD-GSIM [72]) and eleven 3D metrics: Shao2016 [16], Shao2017 [73], Zhou2017 [10], Zhou'2017 [12], Liu2018 [74], Wang2018 [75], Ma2018 [20], Yue2018 [40] Shao2018 [17], Chen2019 [21], and Liu2019 [18], on LIVE 3D Phase I and LIVE 3D Phase II. Works conducted on MCL 3D Database are relatively fewer, so here we take eight works compared with our proposed model on MCL 3D Database: SSIM, BRISQUE, Shao2016, Zhou2017, Liu2018 [74], Yang'2018 [15], Chen2019 and Liu2019. The comparison results are listed in Table 2 and Table 3, and the top two performances are highlighted in boldface.…”

“…The above models are built based on the visual single-channel theory, while the double-channel model is proved to be a more plausible way, which has been applied and proved its effectiveness in our previous SIQA works [13], [14]. Then 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.…”