Abstract
Image quality assessment is a challenge research topic in imaging engineering and applications, especially in the case where the reference image cannot be accessed, such as aerial images. In view of such an issue, a novel learning based evaluation approach was developed. In practice, only objective quality criteria usually cannot achieve desired result. Based on the analysis of multiple objective quality assessment criteria, a boosting algorithm with supervised learning, LassBoost (Learn to Assess with Boosting), was employed to seek the unification of the multiple objective criteria with subjective criteria. This new approach can effectively fuse multiple objective quality criteria guided by the subjective quality level such that the subjective /objective criteria can be unified using weighted regression method. The experimental results illustrate that the proposed method can achieve significantly better performance for image quality assessment, thus can provide a powerful decision support in imaging engineering and practical applications.
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References
Weihong, S., Shi**, C.: A Remote Sensing Image Quality Standard Orienting to User’s Mission Requirements - NIIRS. Spacecraft Recovery & Remote Sensing 24(3), 30–35 (2003)
Qiang, Z.: A Research On the Influence of Satellite Population Parameter to Remote Sensing Image Quality. A Dissertation Submitted to Harbin Institute of Technology for the Master’s Degree (2002)
Riehl, K.: A Historical Review of Reconnaissance Image Evaluation Techniques. SPIE 28(29), 322–333 (1996)
Li, Q., Feng, H., et al.: Research On the Assessment Function of Digital Image’s Sharpnes. Acta Photonica Sinica 31(6), 736–738 (2002)
**u, J.: Aerial Image Quality Evaluation Based On Image Power Spectra. A Dissertation Submitted to the Academy of Sciences for the Degree of Ph.D. of Science (2005)
Delvit, J.-M., Leger, D., et al.: Signal to noise ratio assessment from non-specific view. SPIE 45(52), 40–44 (2002)
Freund, Y., Schapire, R.E.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)
Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (1999)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the 13th International Conference on Machine Learning (1996)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics 28, 1135–1168 (2000)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 854–869 (2007)
Huang, P., Wang, G., Qin, S.: A Novel Learning Approach to Multiple Tasks Based on Boosting Methodology. Pattern Recognition Letters 31(12), 1693–1700 (2010)
Kegl, B., Busa-Fekete, R.: Boosting products of base classifiers. In: Proceedings of the 26th International Conference on Machine Learning (2009)
Busa-Fekete, R., Kegl, B.: Fast boosting using adversarial bandits. In: Proceedings of the 27th International Conference on Machine Learning (2010)
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Huang, P., Qin, S., Lu, D. (2011). A Novel Approach to Image Assessment by Seeking Unification of Subjective and Objective Criteria Based on Supervised Learning. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2011. Lecture Notes in Computer Science, vol 6930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24136-9_24
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DOI: https://doi.org/10.1007/978-3-642-24136-9_24
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