Abstract
Noise level (image quality) evaluation is an important and popular topic in many applications. However, the knowledge of how people visually explore distorted images for making decision on noise evaluation is rather limited. In this paper, we conducted psychophysical eye-tracking studies to deeply understand the process of image noise evaluation. We identified two different types of methodologies in the evaluation processing, speed-driven and accuracy-driven respectively, in terms of both evaluation time and decision error. The speed-driven methodology, compared with the accuracy-driven one, uses less time to give evaluation results, with shorter fixation duration and stronger central bias. Furthermore, based on the utilization of temporal-spatial entropy analysis on eye movement data, a quantitative measure is obtained to show significant correlation with the decision-making efficiency of evaluation processing, which is characterized by evaluation time and decision error. As a result, the new measure may be used as a proxy definition for this decision-making efficiency.
This work has been funded by Natural Science Foundation of China under Grants No. 61471261 and No. 61771335. The author Yuejun Guo acknowledges support from Secretaria dUniversitats i Recerca del Departament dEmpresa i Coneixement de la Generalitat de Catalunya and the European Social Fund.
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Xu, Q., Li, Y., Guo, Y., Wu, S., Sbert, M.: Random-valued impulse noise removal using adaptive ranked-ordered impulse detector. J. Electron. Imaging 27(1), 013001 (2018). https://doi.org/10.1117/1.JEI.27.1.013001
Zhang, W., Liu, H.: Learning picture quality from visual distraction: psychophysical studies and computational models. Neurocomputing 247, 183–191 (2017). https://doi.org/10.1016/j.neucom.2017.03.054
Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., Van de Weijer, J.: Eye Tracking: A Comprehensive Guide to Methods and Measures. OUP, Oxford (2011). ISBN 9780199697083
Engelke, U., Zepernick, H.J., Maeder, A.: Visual fixation patterns in subjective quality assessment: the relative impact of image content and structural distortions. In: 2010 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 1–4. IEEE (2010). https://doi.org/10.1109/ispacs.2010.5704603
Allard, R., Cavanagh, P.: Different processing strategies underlie voluntary averaging in low and high noise. J. Vision 12(11), 6–6 (2012). https://doi.org/10.1167/12.11.6
Min, X., Zhai, G., Gao, Z., Hu, C.: Influence of compression artifacts on visual attention. In: 2014 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2014). https://doi.org/10.1109/icme.2014.6890189
Röhrbein, F., Goddard, P., Schneider, M., James, G., Guo, K.: How does image noise affect actual and predicted human gaze allocation in assessing image quality? Vision. Res. 112, 11–25 (2015). https://doi.org/10.1016/j.visres.2015.03.029
Shiferaw, B., Downey, L., Crewther, D.: A review of gaze entropy as a measure of visual scanning efficiency. Neurosci. Biobehav. Rev. 96, 353–366 (2019). https://doi.org/10.1016/j.neubiorev.2018.12.007
Shojaeizadeh, M., Djamasbi, S., Paffenroth, R.C., Trapp, A.C.: Detecting task demand via an eye tracking machine learning system. Decis. Support Syst. 116, 91–101 (2019). https://doi.org/10.1016/j.dss.2018.10.012
Ponomarenko, N., et al.: Image database TID2013: peculiarities, results and perspectives. Sig. Process. Image Commun. 30, 57–77 (2015). https://doi.org/10.1016/j.image.2014.10.009
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948). https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Feixas, M., Bardera, A., Rigau, J., Xu, Q., Sbert, M.: Information theory tools for image processing. Synth. Lect. Comput. Graph. Animation 6(1), 1–164 (2014). https://doi.org/10.2200/S00560ED1V01Y201312CGR015
Wooding, D.S.: Fixation maps: quantifying eye-movement traces. In: Proceedings of the 2002 Symposium on Eye Tracking Research & Applications, pp. 31–36. ACM (2002). https://doi.org/10.1145/507072.507078
Hammersley, J.: Monte Carlo Methods. Springer, London (2013). https://doi.org/10.1007/978-94-009-5819-7
Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annu. Rev. Neurosci. 24(1), 1193–1216 (2001). https://doi.org/10.1146/annurev.neuro.24.1.1193
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Peng, C., Xu, Q., Guo, Y., Schoeffmann, K. (2019). Eye Movement-Based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_3
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