An Image Quality Assessment Metric with No Reference Using Hidden Markov Tree Model No reference (NR) method is the most difficult issue of image guality 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 NRIQA is a high level evaluation for image quality and simulates the complicated subjective process of human beings. This paper presents a NRIQA 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.

Image Quality Assessment and Human Visual System This paper summaries the stateoftheart of image quality assessment (IQA) and human visual system (HVS). IQA provides an objective index or real value to measure the quality of the specified image. Since human beings are the ultimate receivers of visual information in practical applications, the most reliable IQA is to build a computational model to mimic the HVS. According to the properties and cognitive mechanism of the HVS, the available HVSbased IQA methods can be divided into two categories, i.e., bionics methods and engineering methods. This paper briefly introduces the basic theories and development histories of the above two kinds of HVSbased IQA methods. Finally, some promising research issues are pointed out in the end of the paper.