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16 September 1994 Picture quality evaluation based on error segmentation
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Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994)
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States
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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wen Xu and Gert Hauske "Picture quality evaluation based on error segmentation", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994);

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