A New Quality Metric for Compressed Images based on DDCT As the performanceindicator of the image processing algorithms or systems, image quality assessment (IQA) has attracted the attention of many researchers. Aiming to the widely used compression standards, JPEG and JPEG2000, we propose a new no reference (NR) metric for compressed images to do IQA. This metric exploits the causes of distortion by JPEG and JPEG2000, employs the directional discrete cosine transform (DDCT) to obtain the detail and direction information of the images and incorporates with the visual perception to obtain the image quality index. Experimental results show that the proposed metric not only has outstanding performance on JPEG and JPEG2000 images, but also applicable to other types of artifacts.

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.