An Automatic Analysis Method of Geometric Distortion for Image Quality Objective Evaluation Point Map

Abstract

The invention relates to a geometric distortion automatic analysis method for objectively evaluating a point map of image quality. The method supports automatic complement and search of internal incomplete points, forms a closed loop between the whole target segmentation process and the point map sorting process, achieves the optimization of sorting, and increases the robustness of the distortionanalysis process to the change of the point map segmentation result. Considering that there may be incomplete boundary points in the process of image capturing, which leads to inaccurate position of boundary points in the sorting process, this method adopts the method of region growth to adjust the incomplete boundary points uniformly, which increases the robustness of distortion analysis processto the incomplete degree of boundary points of the point map.

Description

【Technical field】 [0001] The invention relates to the field of objective image quality evaluation, and can be used for an analysis method for automatically extracting and analyzing geometric distortion-related parameters based on dot-map charts in an image objective evaluation system. 【Background technique】 [0002] For the current objective image quality evaluation system, there are two main chart cards used for geometric distortion evaluation, namely Checkerboard and dot chart. This method only makes technical modifications for the detection errors that are prone to occur in the dot chart chart. Therefore, Checkerboard-based geometric distortion analysis is not considered. As the name suggests, except for the monochromatic background, the rest of the charts are circles with the same radius and uniform distribution in space. According to whether the background of the chart is dark or not, it is divided into two types: white dots on black background and black dots on white b...

AI Technical Summary

Problems solved by technology

[0009] 1) When the point number, size and other attributes of the point map change greatly, the result of point segmentation based on Hough transform may not be ideal, which is related to the setting parameters of the Hough transform method. If the point map is too dense or scattered, the segmentation Leakage points and noise points are easy to appear in the results (one point is recognized as multiple points);

[0010] 2) When there are incomplete and noise points, there will be obvious sorting errors when doing point sorting, resulting in inaccurate results from statistical analysis

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