Automatic detection of well sampled images via a new ringing measure

Abstract : According to Shannon Sampling Theory, Fourier interpolation is the optimal way to reach subpixel accuracy from a properly-sampled digital image. However, for most images this interpolation tends to produce an artifact called ringing, that consists in undesirable oscillations near objects contours. In this work, we propose a way to detect this ringing artifact. Using Euler zigzag numbers, we compute the probability that neighboring gray-levels form an alternating sequence by chance, and characterize these undesirable ringing blocks as structures that would be very unlikely in a random image. We then show two applications where the associated algorithm is used to test or enforce the compliance of an image with Fourier interpolation.
Document type :
Conference papers
Complete list of metadatas

Cited literature [7 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00388032
Contributor : Lionel Moisan <>
Submitted on : Thursday, January 7, 2010 - 5:20:56 PM
Last modification on : Thursday, April 11, 2019 - 4:02:09 PM
Long-term archiving on : Thursday, September 23, 2010 - 11:16:56 AM

File

2009-10r2.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Gwendoline Blanchet, Lionel Moisan, Bernard Rougé. Automatic detection of well sampled images via a new ringing measure. 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, Dallas, United States. pp.1030 - 1033, ⟨10.1109/ICASSP.2010.5495324⟩. ⟨hal-00388032v2⟩

Share

Metrics

Record views

310

Files downloads

186