Image Quality Assessment Based on Contourlet and ESD Method

In recent years, the development of the digital image processing promotes the research of the image quality assessment (IQA). A novel metric for full-reference image quality assessment is presented. The metric combined the contourlet transform with the energy of structural distortion (ESD), namely the CT-ESD. The calculation of the ESD is carried out in each subband of the contourlet transform. Then the comparisons between the reference and the distorted images on each subband are integrated by weighting sum. The superiority of the contourlet transform integrates well into new IQA metric. Experiments performed on the database TID2013 demonstrate that the CT-ESD can achieve high consistency with the subjective evaluation

A New Method for Monitoring Gears Surface Failures Using Enhanced Image Registration Approach

In this paper, we present an image registration approach to cope with inter-image illumination changes of arbitrary shape in order to monitor the development of micro-pitting in transmission gears. Traditional image registration approaches do not typically account for inter-image illumination variations that negatively affect the geometric registration precision. Given a set of captured images of gear surface degradation with different exposure times and geometric deformations, the correlation between the resulting aligned images is compared to a reference one. The presented image registration approach is used with an online health monitoring system involving the analysis of vibration, acoustic emission and oil debris to follow the development of micro-pitting in transmission gears. The proposed monitoring system achieves more registration precision compared to competing systems. This paper experimentally validates the system's capabilities to detect early gear defects and reliably identify the gradual development of micro-pitting in gears, so that it could be used in predictive health monitoring (PHM) systems and overcome the disadvantages of the most commonly used methods, such as gear flank profile scanning, replica sample analysis and conventional image analysis

Bridging the Gap Between Imaging Performance and Image Quality Measures

Imaging system performance measures and Image Quality Metrics (IQM) are reviewed from a systems engineering perspective, focusing on spatial quality of still image capture systems. We classify IQMs broadly as: Computational IQMs (CPIQM), Multivariate Formalism IQMs (MF-IQM), Image Fidelity Metrics (IF-IQM), and Signal Transfer Visual IQMs (STV-IQM). Comparison of each genre finds STV-IQMs well suited for capture system quality evaluation: they incorporate performance measures relevant to optical systems design, such as Modulation Transfer Function (MTF) and Noise-Power Spectrum (NPS); their bottom, modular approach enables system components to be optimised separately. We suggest that correlation between STV IQMs and observer quality scores is limited by three factors: current MTF and NPS measures do not characterize scene-dependent performance introduced by imaging system non-linearities; contrast sensitivity models employed do not account for contextual masking effects; cognitive factors are not considered. We hypothesise that implementation of scene and process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures should mitigate errors originating from scene dependent system performance. Further, we propose implementation of contextual contrast detection and discrimination models to better represent low-level visual performance in image quality analysis. Finally, we discuss image quality optimization functions that may potentially close the gap between contrast detection/discrimination and quality