Paper
24 January 2012 Improving texture loss measurement: spatial frequency response based on a colored target
Author Affiliations +
Proceedings Volume 8293, Image Quality and System Performance IX; 829305 (2012) https://doi.org/10.1117/12.907303
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States
Abstract
The pixel race in the digital camera industry and for mobile phone imaging modules have made noise reduction a significant part in the signal processing. Depending on the used algorithms and the underlying amount of noise that has to be removed, noise reduction leads to a loss of low contrast fine details, also known as texture loss. The description of these effects became an important part of the objective image quality evaluation in the last years, as the established methods for noise and resolution measurement fail to do so. Different methods have been developed and presented, but could not fully satisfy the requested stability and correlation with subjective tests. In our paper, we present our experience with the current approaches for texture loss measurement. We have found a critical issue within these methods: the used targets are neutral in color. We could show that the test-lab results do not match the real live experience with the cameras under test. We present an approach using a colored target and our experience with this method.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Uwe Artmann and Dietmar Wueller "Improving texture loss measurement: spatial frequency response based on a colored target", Proc. SPIE 8293, Image Quality and System Performance IX, 829305 (24 January 2012); https://doi.org/10.1117/12.907303
Lens.org Logo
CITATIONS
Cited by 8 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Cameras

Image quality

Spatial frequencies

Denoising

Detection and tracking algorithms

Image analysis

Picosecond phenomena

Back to Top