Objective Quality Assessment of Tone-Mapped Images
By Hojatollah Yeganeh, Zhou Wang. IEEE Transactions on Image Processing, 2013
618 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/c423c5e4c40e360955c105edf85162f2552905b6
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, methodology): “In quantitative comparison, we used PSNR and SSIM [76], along with some recently proposed metrics Average Brightness (AB) [14], Visual Information Fidelity (VIF) [67], Lightness Order Error (LOE) [82], Tone Mapped Image Quality Index (TMQI) [81] and Learned Perceptual Image Patch Similarity Metric (”
- Perceptual Quality Assessment for Multi-Exposure Image Fusion (2015, background): “These include information theoretic and natural scene statistical approaches [38]–[40], the adaptive linear system decomposition framework [41]–[43], the feature similarity method [44], and visual attention and saliency-based approaches [39], [45]–[47].”
- An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, background): “…[303], the Gradient Magnitude Similarity Deviation (GMSD) [304], the visual information fidelity (VIF) [305], the visual saliency index (VSI) [306], the tone-mapped image quality index (TMQI) [307], the Statistical Naturalness Measure (SNM) [307], and the Feature SIMilarity Index (FSIM) [308].”
- MBLLEN: Low-Light Image/Video Enhancement Using CNNs (2018, methodology): “To evaluate the performance of different methods from different aspects and in a more fair way, we use a variety of different metrics, including PSNR, SSIM [39], Average Brightness(AB) [6], Visual Information Fidelity(VIF) [32], Lightness order error(LOE) as suggested in [41] and TMQI [40].”