Video Error Concealment

Agrafiotis, Dimitris

doi:10.1016/b978-0-12-420149-1.00009-0

Cited by 12 publications

(5 citation statements)

References 53 publications

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“…All precipitation outputs were re-gridded using second-order conservative mapping due to the significant variability and unequal distribution of precipitation. In contrast, all temperature outputs were re-gridded using bilinear interpolation 32 to prevent erroneous scale gap effects 31 . All outputs were re-grided to a single 1° × 1° grid.…”

Section: Methodsmentioning

confidence: 99%

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Adeyeri

Zhou

Wang

et al. 2022

Sci Rep

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Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979–2014), near future (2025–2060) and far future (2065–2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions.

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Section: Methodsmentioning

confidence: 99%

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Adeyeri

Zhou

Wang

et al. 2022

Sci Rep

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show abstract

“…The second‐order conservative mapping was used to regrid all precipitation outputs. In contrast, bilinear interpolation was used to regrid all temperature outputs (Agrafiotis, 2014), all to a typical 1° × 1° grid to prevent erroneous scale gap effects (Iturbide et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

et al. 2023

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Understanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias‐corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological‐hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought‐related consequences.

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“…We considered four non-adaptive methods that convert an image from one scale to another. Indeed, the Nearest Neighbor [44], the Bilinear method [45], the Bicubic method [46] and the Lanczos method [47,48] are among the most common interpolation algorithms, and are available in python libraries. Note that the Nearest Neighbor method is the default degradation function on Keras load_img function [35].…”

Section: The Selection Of ρ and Of λmentioning

confidence: 99%

The Noise Blowing-Up Strategy Creates High Quality High Resolution Adversarial Images against Convolutional Neural Networks

Topal,

Mancellari,

Leprévost

et al. 2024

Applied Sciences

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Convolutional neural networks (CNNs) serve as powerful tools in computer vision tasks with extensive applications in daily life. However, they are susceptible to adversarial attacks. Still, attacks can be positive for at least two reasons. First, revealing CNNs’ vulnerabilities prompts efforts to enhance their robustness. Second, adversarial images can also be employed to preserve private and sensitive information from CNN-based threat models aiming to extract such data from images. For such applications, the construction of high-resolution adversarial images is mandatory in practice. This paper achieves the following: first, it quantifies the speed, adversity, and visual quality challenges involved in the effective construction of high-resolution adversarial images; second, it provides the operational design of a new strategy, called here the noise blowing-up strategy, which works for any attack, any scenario, any CNN, and any clean image; third, it validates this strategy via an extensive series of experiments. We performed experiments with 100 high-resolution clean images, exposing them to 7 different attacks against 10 CNNs. Our method achieved an overall average success rate of 75% in the targeted scenario and 64% in the untargeted scenario. We revisited the failed cases and performed a slight modification to our method, which led to success rates larger than 98.9%. As of today, the noise blowing-up strategy is the first generic approach that successfully solves all three speed, adversity, and visual quality challenges, therefore effectively constructing high-resolution adversarial images with high-quality requirements.

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Video Error Concealment

Cited by 12 publications

References 53 publications

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

The Noise Blowing-Up Strategy Creates High Quality High Resolution Adversarial Images against Convolutional Neural Networks

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