Learning the histogram sequences of generalized local ternary patterns for blind image quality assessment

Yaping Yan; Songlin Du; Hongjuan Zhang; Yide Ma

doi:10.1117/12.2228241

9 December 2015 Learning the histogram sequences of generalized local ternary patterns for blind image quality assessment

Yaping Yan, Songlin Du, Hongjuan Zhang, Yide Ma

Author Affiliations +

Proceedings Volume 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015); 981707 (2015) https://doi.org/10.1117/12.2228241
Event: Seventh International Conference on Graphic and Image Processing, 2015, Singapore, Singapore

Abstract

The local binary pattern (LBP) has been proved to be significantly useful and competitive in the application of blind image quality assessment (BIQA). However, LBP is short of magnitude information, limiting its performance to some extent. In this paper, we introduce a novel BIQA method, which uses the proposed generalized local ternary pattern (GLTP) to measure structural degradation. By introducing multi-threshold for the gray-level differences, GLTP can provide more discriminative and stable features. Moreover, GLTP contains magnitude information computed by using the magnitudes of horizontal and vertical first-order derivatives. Experimental results on two subject-rated databases demonstrate that the proposed method outperforms state-of-the-art BIQA models, as well as several representative full reference image quality assessment methods for various types of distortions.

Citation Download Citation

Yaping Yan, Songlin Du, Hongjuan Zhang, and Yide Ma "Learning the histogram sequences of generalized local ternary patterns for blind image quality assessment", Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 981707 (9 December 2015); https://doi.org/10.1117/12.2228241

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