• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0237004 (2025)
Sumei Li* and Huilin Zhang
Author Affiliations
  • School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  • show less
    DOI: 10.3788/LOP241231 Cite this Article Set citation alerts
    Sumei Li, Huilin Zhang. Stereo Image Quality Assessment Based on Top-Down Visual Mechanism[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237004 Copy Citation Text show less
    References

    [1] Union I T[S]. Methodology for the subjective assessment of the quality of television pictures: ITU-R BT. 500-11(2002).

    [2] Union I T[S]. Subjective assessment of stereoscopic television pictures: ITU-R BT.1438(2000).

    [3] Mohona S S, Au D, Kio O G et al. Subjective assessment of stereoscopic image quality: the impact of visually lossless compression[C](2020).

    [4] Hou C P, Lin H H. Stereoscopic image quality assessment based on wavelet transform and structure characteristics[J]. Laser & Optoelectronics Progress, 55, 061005(2018).

    [5] Huang S Y, Sang Q B. No-reference stereo image quality assessment based on image fusion[J]. Laser & Optoelectronics Progress, 56, 071004(2019).

    [6] Ye M M, Hu J B, Wang X J et al. No-reference stereoscopic image quality assessment based on binocular neuron response[J]. Laser & Optoelectronics Progress, 58, 2410007(2021).

    [7] Li S M, Chang Y L, Duan Z C. Objective assessment of stereoscopic image comfort based on convolutional neural network[J]. Acta Optica Sinica, 38, 0610003(2018).

    [8] Chang Y L, Li S M, Hu J J et al. Measurement of comfortable range of stereo image saturation based on salient region[J]. Acta Optica Sinica, 38, 0710003(2018).

    [9] Hu J J, Li S M, Chang Y L et al. Comfortable disparity range of stereo image based on salient region[J]. Acta Optica Sinica, 38, 0811001(2018).

    [10] Fang Y M, Yan J B, Wang J H et al. Learning a no-reference quality predictor of stereoscopic images by visual binocular properties[J]. IEEE Access, 7, 132649-132661(2019).

    [11] Liu Y, Yan W Q, Zheng Z et al. Blind stereoscopic image quality assessment accounting for human monocular visual properties and binocular interactions[J]. IEEE Access, 8, 33666-33678(2020).

    [12] Zhang W, Qu C F, Ma L et al. Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network[J]. Pattern Recognition, 59, 176-187(2016).

    [13] Yue G H, Cheng D, Li L D et al. Semi-supervised authentically distorted image quality assessment with consistency-preserving dual-branch convolutional neural network[J]. IEEE Transactions on Multimedia, 25, 6499-6511(2022).

    [14] Shi J S, Gao P, Qin J. Transformer-based No-reference image quality assessment via supervised contrastive learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 38, 4829-4837(2024).

    [15] Zhou W, Chen Z B, Li W P. Dual-stream interactive networks for no-reference stereoscopic image quality assessment[J]. IEEE Transactions on Image Processing, 28, 3946-3958(2019).

    [16] Yan J B, Fang Y M, Huang L P et al. Blind stereoscopic image quality assessment by deep neural network of multi-level feature fusion[C](2020).

    [17] Shen L L, Chen X F, Pan Z Q et al. No-reference stereoscopic image quality assessment based on global and local content characteristics[J]. Neurocomputing, 424, 132-142(2021).

    [18] Si J W, Huang B X, Yang H et al. A no-reference stereoscopic image quality assessment network based on binocular interaction and fusion mechanisms[J]. IEEE Transactions on Image Processing, 31, 3066-3080(2022).

    [19] Kosslyn S M, Alpert N M, Thompson W L et al. Visual mental imagery activates topographically organized visual cortex: PET investigations[J]. Journal of Cognitive Neuroscience, 5, 263-287(1993).

    [20] Bar M. A cortical mechanism for triggering top-down facilitation in visual object recognition[J]. Journal of Cognitive Neuroscience, 15, 600-609(2003).

    [21] Chang Y L, Li S M, Liu A Q et al. Coarse-to-fine feedback guidance based stereo image quality assessment considering dominant eye fusion[J]. IEEE Transactions on Multimedia, 25, 8855-8867(2023).

    [22] Chang Y L, Li S M, Liu A Q et al. Bidirectional feature aggregation network for stereo image quality assessment considering parallax attention-based binocular fusion[J]. IEEE Transactions on Broadcasting, 70, 278-289(2024).

    [23] Mitchell B A, Dougherty K, Westerberg J A et al. Stimulating both eyes with matching stimuli enhances V1 responses[J]. iScience, 25, 104182(2022).

    [24] Zhang S H, Zhao X N, Tang S M et al. Ocular dominance-dependent binocular combination of monocular neuronal responses in macaque V1[EB/OL]. https://www.biorxiv.org/content/10.1101/2023.10.27.564359v1

    [25] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [26] He K M, Zhang X Y, Ren S Q et al. Identity mappings in deep residual networks[M]. Computer vision-ECCV 2016, 9908, 630-645(2016).

    [27] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

    [28] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018, 11211, 3-19(2018).

    [29] Chen M J, Su C C, Kwon D K et al. Full-reference quality assessment of stereopairs accounting for rivalry[J]. Signal Processing: Image Communication, 28, 1143-1155(2013).

    [30] Chen M J, Cormack L K, Bovik A C. No-reference quality assessment of natural stereopairs[J]. IEEE Transactions on Image Processing, 22, 3379-3391(2013).

    [31] Wang J H, Wang Z. Perceptual quality of asymmetrically distorted stereoscopic images: the role of image distortion types[EB/OL]. https://ece.uwaterloo.ca/~ z70wang/publications/vpqm14.pdf

    [32] Wang J H, Rehman A, Zeng K et al. Quality prediction of asymmetrically distorted stereoscopic 3D images[J]. IEEE Transactions on Image Processing, 24, 3400-3414(2015).

    [33] Sheikh H R, Sabir M F, Bovik A C. A statistical evaluation of recent full reference image quality assessment algorithms[J]. IEEE Transactions on Image Processing, 15, 3440-3451(2006).

    [34] Cao Y, Xu J R, Lin S et al. GCNet: non-local networks meet squeeze-excitation networks and beyond[C], 1971-1980(2019).