The ultimate goal in any proposed Image Quality Metrics (IQMs) is to accurately predict the subjective quality scores given by observers. In the case of most IQMs the quality score is calculated by pooling the quality scores from what is referred to as a quality map of an image. While different pooling methods have been proposed, most such approaches use various types of a weighting average over the quality map to calculate the image quality score. One such approach is to use saliency maps as a weighting factor in our pooling process. Such an approach will result in giving a higher weight to the salient regions of the image. In this work we study if we can evaluate the quality of an image by only calculating the quality of the most salient region in the image. Such an approach could possibly reduce the computational time and power needed for image quality assessment. Results show that in most cases, depending on the saliency calculation method used, we can improve the accuracy of IQMs by simply calculating the quality of a region in the image which covers as low as 20% of the salient energy.
The accuracy of face recognition systems is significantly affected by the quality of face sample images. There are many existing no-reference image quality metrics (IQMs) that are able to assess natural image quality by taking into account similar image-based quality attributes. Previous study showed that IQMs can assess face sample quality according to the biometric system performance. In addition, re-training an IQM can improve its performance for face biometric images. However, only one database was used in the previous study, and it contains only image-based distortions. In this paper, we propose to extend the previous study by use multiple face database including FERET color face database, and apply multiple setups for the re-training process in order to investigate how the re-training process affect the performance of no-reference image quality metric for face biometric images. The experimental results show that the performance of the appropriate IQM can be improved for multiple databases, and different re-training setups can influence the IQM’s performance.