Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
Perceptual quality metrics applied to still image compression
Signal Processing - Special issue on image and video quality metrics
Evaluating the multivariate visual quality performance of image-processing components
ACM Transactions on Applied Perception (TAP)
Descriptive quality of experience for mobile 3D video
Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries
Bayesian network model of overall print quality: Construction and structural optimisation
Pattern Recognition Letters
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Test image contents affect subjective image-quality evaluations. Psychometric methods might show that contents have an influence on image quality, but they do not tell what this influence is like, i.e., how the contents influence image quality. To obtain a holistic description of subjective image quality, we have used an interpretation-based quality (IBQ) estimation approach, which combines qualitative and quantitative methodology. The method enables simultaneous examination of psychometric results and the subjective meanings related to the perceived image-quality changes. In this way, the relationship between subjective feature detection, subjective preferences, and interpretations are revealed. We report a study that shows that different impressions are conveyed in five test image contents after similar sharpness variations. Thirty naïve observers classified and freely described the images after which magnitude estimation was used to verify that they distinguished the changes in the images. The data suggest that in the case of high image quality, the test image selection is crucial. If subjective evaluation is limited only to technical defects in test images, important subjective information of image-quality experience is lost. The approach described here can be used to examine image quality and it will help image scientists to evaluate their test images.