No-reference image quality assessment using modified extreme learning machine classifier

S. Suresh, R. Venkatesh Babu, H. J. Kim

Research output: Contribution to journalArticlepeer-review

221 Citations (Scopus)


In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Original languageEnglish
Pages (from-to)541-552
Number of pages12
JournalApplied Soft Computing Journal
Issue number2
Publication statusPublished - 2009 Mar

Bibliographical note

Funding Information:
This work was in part supported by the ITRC, Korea University, Korea, under the auspices of the Ministry of Information and Communication. The authors would also like to thank Prof. Bovik and his lab members for providing the JPEG image quality assessment database to test our metric.


  • Blockiness measurement
  • Evolutionary algorithms
  • Extreme learning machine
  • Image quality assessment
  • JPEG
  • Neural network
  • No-reference metric

ASJC Scopus subject areas

  • Software


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