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Adapting Pretrained Networks for Image Quality Assessment on High Dynamic Range Displays

Autor: Chubarau, Andrei, Yoo, Hyunjin, Akhavan, Tara, Clark, James

Conventional image quality metrics (IQMs), such as PSNR and SSIM, are designed for perceptually uniform gamma-encoded pixel values and cannot be directly applied to perceptually non-uniform linear high-dynamic-range (HDR) colors. Similarly, most of t

Externí odkaz: http://arxiv.org/abs/2405.00670

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Learning Neural Implicit Representations with Surface Signal Parameterizations

Autor: Guan, Yanran, Chubarau, Andrei, Rao, Ruby, Nowrouzezahrai, Derek

Neural implicit surface representations have recently emerged as popular alternative to explicit 3D object encodings, such as polygonal meshes, tabulated points, or voxels. While significant work has improved the geometric fidelity of these represent

Externí odkaz: http://arxiv.org/abs/2211.00519

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VTAMIQ: Transformers for Attention Modulated Image Quality Assessment

Autor: Chubarau, Andrei, Clark, James

Following the major successes of self-attention and Transformers for image analysis, we investigate the use of such attention mechanisms in the context of Image Quality Assessment (IQA) and propose a novel full-reference IQA method, Vision Transforme

Externí odkaz: http://arxiv.org/abs/2110.01655

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