Objective distortion measure for binary text image based on edge line segment similarity.
This paper proposes a new approach to measure the distortion introduced by changing individual edge pixels in binary text images. The approach considers not only how many pixels are changed but also where the pixels are changed and how the flipping affects the overall shape formed by the edge line. Similarities between the edge line segments in the original and distorted image are compared to measure the distortion. Subjective testing shows that the new distortion measure correlates well with human visual perception.
Cheng
Jun
J
Kot
Alex C
AC
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003196:Computer Graphics; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D011327:Printing / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique