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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Journal ArticleDOI
Hdr-vqm
TL;DR: An objective HDR video quality measure (HDR-VQM) based on signal pre-processing, transformation, and subsequent frequency based decomposition is presented, which is one of the first objective method for high dynamic range video quality estimation.
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Fourier Transform-Based Scalable Image Quality Measure
TL;DR: A new image quality assessment algorithm based on the phase and magnitude of the 2-D discrete Fourier transform that is overall better than several of the existing full-reference algorithms and two RR algorithms and further scalable for RR scenarios.
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Low-Complexity Video Quality Assessment Using Temporal Quality Variations
TL;DR: The proposed full-reference (FR) algorithm is more efficient due to its low complexity without jeopardizing the prediction accuracy and cross-database tests have been carried out to provide a proper perspective of the performance of this scheme as compared to other VQA methods.
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Lorentzian Based Adaptive Filters for Impulsive Noise Environments
Rajib Lochan Das,Manish Narwaria +1 more
TL;DR: Simulation results show that the Lorentzian variable hard thresholding adaptive filtering (LVHTAF) outperforms the existing robust sparse adaptive algorithms by producing lesser steady state mean square error.
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Tone mapping-based high-dynamic-range image compression: study of optimization criterion and perceptual quality
TL;DR: This study investigates two objective optimization criteria, namely mean squared error and structural similarity index measure, toward optimization of a tone mapping model-based HDR image compression method and conducts a comprehensive subjective study to evaluate the visual quality of the compressed HDR images.