No-Reference Metrics for Jpeg: Analysis and Refinement Using Wavelets

No-reference quality metrics estimate the perceived quality exploiting only the image itself. Typically, no-reference metrics are designed to measure specific artifacts using a distortion model. Some psycho-visual experiments have shown that the perception of distortions is influenced by the amount of details in the image's content, suggesting the need for a "content weighting factor." This dependency is coherent with known masking effects of the human visual system. In order to explore this phenomenon, we setup a series of experiments applying regression trees to the problem of no-reference quality assessment. In particular, we have focused on the blocking distortion of JPEG compressed images. Experimental results show that information about the visual content of the image can be exploited to improve the estimation of the quality of JPEG compressed images.