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Feedforward and Feedback Modulations Based Foveated JND Estimation for Images

Published: 16 March 2023 Publication History

Abstract

The just noticeable difference (JND) reveals the key characteristic of visual perception, which has been widely used in many perception-based image and video applications. Nevertheless, the modulatory mechanism of the human visual system (HVS) has not been fully exploited in JND threshold estimation, which results in the existing JND models not being accurate enough. In this article, by analyzing the feedforward and feedback modulatory behaviors in the HVS, an enhanced foveated JND (FJND) estimation model is proposed considering modulatory effects and masking effects in visual perception. The contributions of this article are mainly twofold. On the one hand, by analyzing the modulatory behaviors in the HVS, the modulatory mechanism is incorporated into JND estimation and a hierarchical modulation-based JND estimation framework is proposed for the first time. On the other hand, according to the response characteristics of visual neurons, modulatory effects on visual sensitivity are formulated as several modulatory factors to modulate the estimated JND threshold properly. Compared with existing models, the proposed model is developed in view of not only the masking effects but also the modulatory effects, which makes our model more consistent with the HVS. For different complex input images, experimental results show that the proposed FJND model tolerates more distortion at the same perceptual quality in comparison with other existing models.

References

[1]
USC-SIPI. 2011. The USC-SIPI Image Database. Retrieved January 5, 2023 from https://sipi.usc.edu/database/database.php/.
[2]
IRCCyN/IVC. 2013. Subjective Quality Assessment—IVC Database. Retrieved January 5, 2023 from IRCCyN/IVC Image Quality Database - QUALINET Databases.
[3]
A. J. Ahumada and H. A. Peterson. 1992. Luminance-model-based DCT quantization for color image compression. Proceedings of SPIE: Human Vision, Visual Processing, and Digital Display III 1666 (1992), 365–374.
[4]
S. Bae and K. Munchurl. 2017. A DCT-based total JND profile for spatiotemporal and foveated masking effects. IEEE Transactions on Circuits and Systems for Video Technology 27, 6 (2017), 1196–1207.
[5]
H. B. Barlow. 1961. Possible principle underlying the transformation of sensory messages. In Sensory Communication, W. A. Rosenblith (Ed.). MIT Press, Cambridge, MA, 217–234.
[6]
Z. Chen and C. Guillemot. 2010. Perceptually-friendly H.264/AVC video coding based on foveated just-noticeable-distortion model. IEEE Transactions on Circuits and Systems for Video Technology 20, 6 (2010), 806–819.
[7]
Daniel Huber, Leopoldo Petreanu, Nima Ghitani, Sachin Ranade, Tomas Hromadka, Zach Mainen, and Karel Svoboda. 2008. Sparse optical microstimulation in barrel cortex drives learned behaviour in freely moving mice. Nature 451, 7174 (2008), 61.
[8]
H. Feldman and K. L. Friston. 2010. Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience 4, 1 (2010), 125.
[9]
H. Feng, M. W. Marcellin, and A. Bilgin.2015. A methodology for visually lossless JPEG2000 compression of monochrome stereo images. IEEE Transactions on Image Processing 24, 2 (2015), 560–572.
[10]
Karl Friston. 2010. The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11, 2 (2010), 127.
[11]
Wilson S. Geisler and Jeffrey S. Perry. 1998. Real-time foveated multiresolution system for low-bandwidth video communication. Proceedings of SPIE: Human Vision and Electronic Imaging III 3299 (1998), 294–305.
[12]
K. Gu, G. Zhai, X. Yang, and W. Zhang. 2014. Using free energy principle for blind image quality assessment. IEEE Transactions on Multimedia 17, 1 (2014), 50–63.
[13]
S. Hochstein and M. Ahissar. 2002. View from the top: Hierarchies and reverse hierarchies in the visual system. Neuron 36, 5 (2002), 791–804.
[14]
Laurent Itti and Pierre Baldi. 2009. Bayesian surprise attracts human attention. Vision Research 49, 10 (2009), 1295–1306.
[15]
ITU. 2002. Methodology for the Subjective Assessment of the Quality of Television Pictures. Technical Report ITU-R BT.500-11. ITU, Geneva, Switzerland.
[16]
T. Judd, K. Ehinger, F. Durand, and A. Torralba. 2010. Learning to predict where humans look. In Proceedings of the International Conference on Computer Vision.
[17]
Karl Friston. 2009. The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences 13, 7 (2009), 293–301.
[18]
Sehwan Ki, Sung Ho Bae, Munchurl Kim, and Hyunsuk Ko. 2018. Learning-based just-noticeable-quantization–distortion modeling for perceptual video coding. IEEE Transactions on Image Processing 27, 7 (2018), 3178–3193.
[19]
David C. Knill and Alexandre Pouget. 2004. The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences 27, 12 (2004), 712–719.
[20]
Junlin Li, Li Yu, and Hongkui Wang. 2022. Perceptual redundancy model for compression of screen content videos. IET Image Processing 16, 6 (2022), 1724–1741.
[21]
Anmin Liu, Weisi Lin, Manoranjan Paul, Chenwei Deng, and Fan Zhang. 2010. Just noticeable difference for images with decomposition model separating edge and textured regions. IEEE Transactions on Circuits and Systems for Video Technology 20, 11 (2010), 1648–1652.
[22]
Huanhua Liu, Yun Zhang, Huan Zhang, Chunling Fan, and Xiaoping Fan. 2019. Deep learning-based picture-wise just noticeable distortion prediction model for image compression. IEEE Transactions on Image Processing 29 (2019), 641–656.
[23]
S. L. Macknik and M. S. Livingstone. 1998. Neuronal correlates of visibility and invisibility in the primate visual system. Nature Neuroscience 1, 2 (1998), 144–149.
[24]
D. J. Mannion, D. J. Kersten, C. A. Olman, and J. Foxe. 2015. Scene coherence can affect the local response to natural images in human V1. European Journal of Neuroscience 42, 11 (2015), 2895–2903.
[25]
T. Oosuga, M. Tanaka, H. Inoue, and Y. Niiyama. 2012. A study on eye fixation time distribution with and without subjective evaluation of food and related pictures. In Proceedings of the 2012 SICE Annual Conference (SICE’12).
[26]
N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti. 2013. Color image database TID2013: Peculiarities and preliminary results. In Proceedings of the European Workshop on Visual Information Processing.
[27]
R. P. Rao and D. H. Ballard. 1999. Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2, 11 (1999), 79–87.
[28]
Kais Rouis, Mohamed-Chaker Larabi, and Jamel Belhadj Tahar. 2018. Perceptually adaptive Lagrangian multiplier for HEVC guided rate-distortion optimization. IEEE Access 6 (2018), 33589–33603.
[29]
Bryan C. Russell, Antonio Torralba, Kevin P. Murphy, and William T. Freeman. 2008. LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision 77, 1-3 (2008), 157–173.
[30]
Masaki Sano. 2015. Experimental demonstration of information-to-energy conversion in small fluctuating systems. In Proceedings of the APS Meeting.
[31]
Tao Tian, Hanli Wang, Lingxuan Zuo, C.-C. Jay Kuo, and Sam Kwong. 2020. Just noticeable difference level prediction for perceptual image compression. IEEE Transactions on Broadcasting 66, 3 (2020), 690–700.
[32]
P. Wallisch and J. A. Movshon. 2008. Structure and function come unglued in the visual cortex. Neuron 8, 2 (2008), 195–197.
[33]
Hongkui Wang, Shengwei Wang, Tiansong Li, Haibing Yin, and Li Yu. 2019. Surprise based JND estimation for images. IEEE Signal Processing Letters 27 (2019), 181–184.
[34]
Hongkui Wang, Li Yu, Junhui Liang, Haibing Yin, Tiansong Li, and Shengwei Wang. 2021. Hierarchical predictive coding-based JND estimation for image compression. IEEE Transactions on Image Processing 30 (2021), 487–500.
[35]
Hongkui Wang, Li Yu, Shengwei Wang, Tiansong Li, and Haibing Yin. 2018. A novel foveated-JND profile based on an adaptive foveated weighting model. In Proceedings of the 2018 IEEE Conference on Visual Communications and Image Processing (VCIP’18).
[36]
Hongkui Wang, Li Yu, Haibing Yin, Tiansong Li, and Shengwei Wang. 2020. An improved DCT-based JND estimation model considering multiple masking effects. Journal of Visual Communication and Image Representation 71 (2020), 102850.
[37]
Rubin Wang and Zhikang Zhang. 2007. Energy coding in biological neural networks. Cognitive Neurodynamics 1, 3 (2007), 203–212.
[38]
Rubin Wang, Zhikang Zhang, and Guanrong Chen. 2009. Energy coding and energy functions for local activities of the brain. Neurocomputing 73, 1-3 (2009), 139–150.
[39]
S. Wang, L. Ma, Y. M. Fang, W. Lin, S. Ma, and W. Gao. 2016. Just noticeable difference estimation for screen content images. IEEE Transactions on Image Processing 25, 8 (2016), 3838–3851.
[40]
Zhou Wang, Ligang Lu, and A. C. Bovik. 2003. Foveation scalable video coding with automatic fixation selection. IEEE Transactions on Image Processing 12, 2 (2003), 243–254.
[41]
Zhenyu Wei and King N. Ngan. 2009. Spatio-temporal just noticeable distortion profile for grey scale image/video in DCT domain. IEEE Transactions on Circuits and Systems for Video Technology 19, 3 (2009), 337–346.
[42]
J. Wu, L. Li, W. Dong, G. Shi, W. Lin, and C.-C. Jay Kuo. 2017. Enhanced just noticeable difference model for images with pattern complexity. IEEE Transactions on Image Processing 26, 6 (2017), 2682–2693.
[43]
J. Wu, W. Lin, G. Shi, X. Wang, and F. Qi. 2013. Just difference estimation for images with free energy principle. IEEE Transactions on Image Processing 15, 7 (2013), 1705–1710.
[44]
J. Wu, G. Shi, W. Lin, A. Liu, and F. Li. 2013. Pattern masking estimation in image with structural uncertainty. IEEE Transactions on Image Processing 22, 12 (2013), 4892–4904.
[45]
Kaifu Yang, Chaoyi Li, and Yongjie Li. 2014. Multi feature-based surround inhibition improves contour detection in natural images. IEEE Transactions on Image Processing 23, 12 (2014), 5020–5032.
[46]
X. Yang, W. Lin, Z. Lu, E. Ong, and S. Yao. 2005. Just noticeable distortion model and its applications in video coding. Signal Processing: Image Communication 20 (2005), 662–680.
[47]
Zhipeng Zeng, Huanqiang Zeng, Jing Chen, Jianqing Zhu, Yun Zhang, and Kai Kuang Ma. 2019. Visual attention guided pixel-wise just noticeable difference model. IEEE Access 7 (2019), 132111–132119.
[48]
G. Zhai, X. Wu, X. Yang, W. Lin, and W. Zhang. 2012. A psychovisual quality metric in free-energy principle. IEEE Transactions on Image Processing 21, 1 (2012), 41–52.
[49]
Xinfeng Zhang, Shiqi Wang, Ke Gu, Weisi Lin, Siwei Ma, and Wen Gao. 2017. Just-noticeable difference-based perceptual optimization for JPEG compression. IEEE Signal Processing Letters 24, 1 (2017), 96–100.
[50]
Yun Zhang, Huanhua Liu, You Yang, Xiaoping Fan, Sam Kwong, and C. C. Jay Kuo. 2022. Deep learning based just noticeable difference and perceptual quality prediction models for compressed video. IEEE Transactions on Circuits and Systems for Video Technology 32, 3 (2022), 1197–1212.
[51]
Mingliang Zhou, Xuekai Wei, Shiqi Wang, Sam Kwong, Chi Keung Fong, Peter Wong, Wilson Yuen, and Wei Gao. 2019. SSIM-based global optimization for CTU-level rate control in HEVC. IEEE Transactions on Multimedia 21, 8 (2019), 1921–1933.

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    Published In

    cover image ACM Transactions on Multimedia Computing, Communications, and Applications
    ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 5
    September 2023
    262 pages
    ISSN:1551-6857
    EISSN:1551-6865
    DOI:10.1145/3585398
    • Editor:
    • Abdulmotaleb El Saddik
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 March 2023
    Online AM: 04 January 2023
    Accepted: 18 December 2022
    Revised: 25 October 2022
    Received: 08 June 2022
    Published in TOMM Volume 19, Issue 5

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    Author Tags

    1. JND estimation model
    2. visual attention
    3. foveated masking
    4. feedforward and feedback modulatory effects

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    • “Pioneer” and “Leading Goose” R&D Program of Zhejiang Province
    • NSFC
    • Natural Science Foundation of Hubei Province of China

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