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.
A novel image segmentation algorithm which uses quantum entropy and pulse-coupled neural networks (PCNN) is proposed in this paper. Optimal iteration of the PCNN is one of the key factors affecting segmentation accuracy. We borrow quantum entropy from quantum information to act as a criterion in determining optimal iteration of the PCNN. Optimal iteration is captured while total quantum entropy of the segments reaches a maximum. Moreover, compared with other PCNN-employed algorithms, the proposed algorithm works without any manual intervention, because all parameters of the PCNN are set automatically. Experimental results prove that the proposed method can achieve much lower probabilities of error segmentation than other PCNN-based image segmentation algorithms, and this suggests that higher image segmentation quality is achieved by the proposed method.