All-digital ring–wedge detector applied to image quality assessment

Abstract

In the automatic assessment of image quality we obtained a high accuracy in the classification of image degradations in a manner that is widely independent of scene content. Using an all-digital ring-wedge detector system combined with neural-network software, we conducted several experiments in which the end goal is to classify images according to numerical quality scales. Experiments are presented to stress the importance of both local and global image quality assessment. Two databases of degraded images were prepared. One uses five levels of Gaussian blur to simulate depth of field. The other was prepared with lossy compression and recovery with artifacts generated by a JPEG (Joint Photographic Experts Group) compression algorithm. In quantitative terms our best sorting of Gaussian blur without knowledge of the original scene was to an accuracy of 96%. For degradation using JPEG we obtained an accuracy of 95% without knowledge of the original and 98% when the original scene is available as a reference.

Modified geometry of ring-wedge detector for sampling Fourier transform of fingerprints for classification using neural networks

“…George et al [5] used RWD and neural networks software to illustrate classification of thumbprints. George and Wang [6] and Berfanger and George [7] applied this to fingerprints sorting. Hexagonal FT of fingerprints was sampled using a RWD by Fitz and Green [8].…”

“…Another area of application for RWD is particulate analysis and image quality assessment [7,8]. Coston and George [11] applied RWD for recovery of particle size distribution.…”