Image quality assessment by discrete orthogonal moments

  • Authors:

  • Chong-Yaw Wee;Raveendran Paramesran;R. Mukundan;Xudong Jiang

  • Affiliations:

  • Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia and School of Electrical and Electronic Engineering, Nanyang Technol ...;Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia;Department of Computer Science and Software Engineering, University of Canterbury, Private Bag 4800, Christchurch 8020, New Zealand;School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore, Singapore

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

This paper proposes a novel full-reference quality assessment (QA) metric that automatically assesses the quality of an image in the discrete orthogonal moments domain. This metric is constructed by representing the spatial information of an image using low order moments. The computation, up to fourth order moments, is performed on each individual (8x8) non-overlapping block for both the test and reference images. Then, the computed moments of both the test and reference images are combined in order to determine the moment correlation index of each block in each order. The number of moment correlation indices used in this study is nine. Next, the mean of each moment correlation index is computed and thereafter the single quality interpretation of the test image with respect to its reference is determined by taking the mean value of the computed means of all the moment correlation indices. The proposed objective metrics based on two discrete orthogonal moments, Tchebichef and Krawtchouk moments, are developed and their performances are evaluated by comparing them with subjective ratings on several publicly available databases. The proposed discrete orthogonal moments based metric performs competitively well with the state-of-the-art models in terms of quality prediction while outperforms them in terms of computational speed.