An image quality assessment metric with no reference using hidden Markov tree model

Fei Gao; Xinbo Gao; Wen Lu; Dacheng Tao; Xuelong Li

doi:10.1117/12.862433

4 August 2010 An image quality assessment metric with no reference using hidden Markov tree model

Fei Gao, Xinbo Gao, Wen Lu, Dacheng Tao, Xuelong Li

Proceedings Volume 7744, Visual Communications and Image Processing 2010; 774410 (2010) https://doi.org/10.1117/12.862433
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China

Abstract

No reference (NR) method is the most difficult issue of image quality assessment (IQA), which does not need the original image or its features as reference and only depends on the statistical law of the natural images. So, the NR-IQA is a high -level evaluation for image quality and simulates the complicated subjective process of human beings. This paper presents a NR-IQA metric based on Hidden Markov Tree (HMT) model. First, the HMT is utilized to model natural images, and the statistical properties of the model parameters are analyzed to mimic variation of image degradation. Then, by estimating the deviation degree of the parameters from the statistical law the distortion metric is constructed. Experimental results show that the proposed image quality assessment model is consistent well with the subjective evaluation results, and outperforms the existing models on difference distortions.

Citation Download Citation

Fei Gao, Xinbo Gao, Wen Lu, Dacheng Tao, and Xuelong Li "An image quality assessment metric with no reference using hidden Markov tree model", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 774410 (4 August 2010); https://doi.org/10.1117/12.862433

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