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Comparative Study of Classifiers for Blurred Images

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Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1229))

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Abstract

In this paper, we want to launch a first step for the classification of images according to their degree of blur based on the subjective measurement of DMOS image quality in the Gblur database. For this purpose, we have carried out a comparative study on several classifiers in order to build a robust learning model based on the transformation into a DCT. The class imbalanced has forced us to look for and find appropriate performance evaluation metrics so that the comparison is not biased. It has been found that random forests (RF) give the best overall performance but that other classifiers discriminate better between certain types of images (depending on the degree of blur) than others. Finally, we compared the classification by the proposed model with another classification based on the NIQE quality measurement algorithm. The results of the model proposed by its simplicity are very promising.

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Author information

Authors and Affiliations

  1. Image Processing and Radiations Laboratory (LTIR), Houari Boumediene University of Science and Technology (USTHB), Algiers, Algeria

    Ratiba Gueraichi & Amina Serir

Authors
  1. Ratiba Gueraichi
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  2. Amina Serir
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Corresponding author

Correspondence to Ratiba Gueraichi .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Gueraichi, R., Serir, A. (2020). Comparative Study of Classifiers for Blurred Images. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_23

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