M
Manish Narwaria
Researcher at Centre national de la recherche scientifique
Publications - 43
Citations - 2124
Manish Narwaria is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Tone mapping & Human visual system model. The author has an hindex of 19, co-authored 41 publications receiving 1828 citations. Previous affiliations of Manish Narwaria include Nanyang Technological University & University of Nantes.
Papers
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Image Quality Assessment Based on Gradient Similarity
TL;DR: The proposed IQA scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme.
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HDR-VDP-2.2: a calibrated method for objective quality prediction of high-dynamic range and standard images
TL;DR: The main contribution is toward improving the frequency-based pooling in HDR-VDP-2 to enhance its objective quality prediction accuracy by formulating and solving a constrained optimization problem and thereby finding the optimal pooling weights.
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Saliency Detection for Stereoscopic Images
TL;DR: A new stereoscopic saliency detection framework based on the feature contrast of color, intensity, texture, and depth, which shows superior performance over other existing ones in saliency estimation for 3D images is proposed.
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Objective Image Quality Assessment Based on Support Vector Regression
Manish Narwaria,Weisi Lin +1 more
TL;DR: A new approach to address the problem of objective image quality estimation, with the use of singular vectors out of singular value decomposition (SVD) as features for quantifying major structural information in images and then support vector regression (SVR) for automatic prediction of image quality.
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SVD-Based Quality Metric for Image and Video Using Machine Learning
Manish Narwaria,Weisi Lin +1 more
TL;DR: The two-stage process and the relevant work in the existing visual quality metrics are first introduced followed by an in-depth analysis of SVD for visual quality assessment, which shows the proposed method outperforms the eight existing relevant schemes.