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Preference of Experience in Image Tone-Mapping: Dataset and Framework for Objective Measures Comparison.

, , , and . J. Sel. Topics Signal Processing, 11 (1): 64-74 (2017)

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On improving the pooling in HDR-VDP-2 towards better HDR perceptual quality assessment., , , and . Human Vision and Electronic Imaging, volume 9014 of SPIE Proceedings, page 90140N. SPIE, (2014)Toward Better Statistical Validation of Machine Learning-Based Multimedia Quality Estimators.. TBC, 64 (2): 446-460 (2018)HDR-VQM: An objective quality measure for high dynamic range video., , and . Signal Process. Image Commun., (2015)SVD-Based Quality Metric for Image and Video Using Machine Learning., and . IEEE Trans. Syst. Man Cybern. Part B, 42 (2): 347-364 (2012)Nonintrusive Quality Assessment of Noise Suppressed Speech With Mel-Filtered Energies and Support Vector Regression., , , , and . IEEE Trans. Speech Audio Process., 20 (4): 1217-1232 (2012)Upsampling of Personalized HRTF for Spatial Audio Rendering: Why Deep Learning is Problematic?, and . COMAD/CODS, page 274-275. ACM, (2023)Does explainable machine learning uncover the black box in vision applications?. Image Vis. Comput., (2022)Scalable image quality assessment based on structural vectors., and . MMSP, page 1-6. IEEE, (2009)Preference of Experience in Image Tone-Mapping: Dataset and Framework for Objective Measures Comparison., , , and . J. Sel. Topics Signal Processing, 11 (1): 64-74 (2017)Adaptive contrast adjustment for postprocessing of tone mapped high dynamic range images., , , and . ISCAS, page 1103-1106. IEEE, (2013)