Picture quality evaluation based on error segmentation

A segmentation-based error metric (SEM) is proposed to evaluate the quality of pictures with impairments resulting from typical source coding algorithms and channel interference. After appropriate visual preprocessing, the error picture is segmented into errors on own edges of the picture, errors representing exotic or spurious edges, and remaining errors in flat regions to describe edge errors like blurring, exotic structures like blocking and contouring, and residual errors like random noise, respectively. Error parameters or distortion factors are derived by appropriate summation over the segmented components. The distortion metric is built by a combination of the parameters using a generalized multiple linear regression procedure. Tests with a picture data base consisting of impairments from various picture coding techniques applied to different types of pictures have shown that the SEM yields very promising results. The correlation coefficient with subjective ratings was 0.875, whereas the widely used PSNR had only a correlation of 0.653. In addition, it is also possible to classify type and amount of individual distortions.