Inspection and evaluation of artifacts in digital video sources

Streaming digital video content providers such as YouTube, Amazon, Hulu, and Netflix collaborate with production teams to obtain new and old video content. These collaborations lead to an accumulation of video sources, some of which might contain unacceptable visual artifacts. Artifacts may inadvertently enter the video master at any point in the production pipeline, due to any of a number of equipment and user failures. Unfortunately, these artifacts are difficult to detect since no pristine reference exists for comparison. As of now, few automated tools exist that can effectively capture the most common forms of these artifacts. This work studies no-reference video source inspection for generalized artifact detection and subjective quality prediction, which will ultimate inform decisions related to acquisition of new content. Automatically identifying the locations and severities of video artifacts is a difficult problem. We have developed a general method for detecting local artifacts by learning differences in the statistics between distorted and pristine video frames. Our model, which we call the Video Impairment Mapper (VID-MAP), produces a full resolution map of artifact detection probabilities based on comparisons of excitatory and inhibatory convolutional responses. Validation on a large database shows that our method outperforms the previous state-of-the-art of even distortion-specific detectors. A variety of powerful picture quality predictors are available that rely on neuro-statistical models of distortion perception. We extend these principles to video source inspection, by coupling spatial divisive normalization with a series of filterbanks tuned for artifact detection, implemented using a common convolutional framework. We developed the Video Impairment Detection by SParse Error CapTure (VIDSPECT) model, which leverages discriminative sparse dictionaries that are tuned to detect specific artifacts. VIDSPECT is simple, highly generalizable, and yields better accuracy than competing methods. To evaluate the perceived quality of video sources containing artifacts, we built a new digital video database, called the LIVE Video Masters Database, which contains 384 videos affected by the types of artifacts encountered in otherwise pristine digital video sources. We find that VIDSPECT delivers top performance on this database for most artifacts tested, and competitive performance otherwise, using the same basic architecture in all cases.Electrical and Computer Engineerin

Super-resolution assessment and detection

Super Resolution (SR) techniques are powerful digital manipulation tools that have significantly impacted various industries due to their ability to enhance the resolution of lower quality images and videos. Yet, the real-world adaptation of SR models poses numerous challenges, which blind SR models aim to overcome by emulating complex real-world degradations. In this thesis, we investigate these SR techniques, with a particular focus on comparing the performance of blind models to their non-blind counterparts under various conditions. Despite recent progress, the proliferation of SR techniques raises concerns about their potential misuse. These methods can easily manipulate real digital content and create misrepresentations, which highlights the need for robust SR detection mechanisms. In our study, we analyze the limitations of current SR detection techniques and propose a new detection system that exhibits higher performance in discerning real and upscaled videos. Moreover, we conduct several experiments to gain insights into the strengths and weaknesses of the detection models, providing a better understanding of their behavior and limitations. Particularly, we target 4K videos, which are rapidly becoming the standard resolution in various fields such as streaming services, gaming, and content creation. As part of our research, we have created and utilized a unique dataset in 4K resolution, specifically designed to facilitate the investigation of SR techniques and their detection

CFD code comparison for 2D airfoil flows

The current paper presents the effort, in the EU AVATAR project, to establish the necessary requirements to obtain consistent lift over drag ratios among seven CFD codes. The flow around a 2D airfoil case is studied, for both transitional and fully turbulent conditions at Reynolds numbers of 3 × 106 and 15 × 106. The necessary grid resolution, domain size, and iterative convergence criteria to have consistent results are discussed, and suggestions are given for best practice. For the fully turbulent results four out of seven codes provide consistent results. For the laminar-turbulent transitional results only three out of seven provided results, and the agreement is generally lower than for the fully turbulent case

Reservoir Characterisation: - Multi-Scales Permeability Data Integration: Lake Albert Basin, Uganda

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A Comprehensive Review of Deep Learning-based Single Image Super-resolution

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.Comment: 56 Pages, 11 Figures, 5 Table

On the optimization of CO2-EOR process using surrogate reservoir model

CO2-EOR projects are becoming increasingly popular. Since enhanced recovery processes are applied to the mature fields, it usually involves a large number of wells. While the large number of wells leads to a better geological model, it results in very large flow models that are hard to manage, history match, and use as an optimization base. Nevertheless, injection-production optimization remains the core of all modeling efforts in CO2-EOR projects.;The objective of this work is to investigate the feasibility of using state-of-the-art data-driven proxy models to facilitate injection-production optimization in a CO2-EOR process. The use of coupled grid-based---SRM G and well-based---SRMW Surrogate Reservoir Model (as a proxy that runs in seconds) will be investigated as a tool to achieve the objectives of this study. The coupled SRM is built based on a reservoir simulation model that is developed for this purpose. The coupled SRM will be able to identify the dynamic reservoir properties (pressure, saturations, and component mole fraction at gridblock level) throughout the reservoir, along with the production characteristics at each well. It can be used to identify the optimum injection strategy (volume, rate, etc.) that would result in increased oil production.;The EOR technique that is attracting the most new market interest is CO2-EOR. First tried in 1972 in Scurry County, Texas, CO2 injection has been used successfully throughout the Permian Basin of West Texas and eastern New Mexico. The SACROC field, a depleted oil field located in western Scurry County in Texas, is the subject of this study.;A high resolution geological model was built for the northern platform. The model is based on a comprehensive geological study including 3D seismic survey and well logs. The porosity and permeability data for the fine grids were obtained from the Bureau of Economic Geology (BEG). The very long run-time of the reservoir simulation model that is the result of complexity of the reservoir makes it impractical to perform any sensitivity analysis, uncertainty analysis, or optimization study on the model. In order to overcome this problem, developing a surrogate reservoir model based on Artificial Intelligence and Data Mining techniques was planned. The coupled SRM provides the means for performing a large number of simulation runs, in short period of time, to be used for uncertainty quantification, and search of solution space for optimization.;Multiple injection scenarios were designed and run using a numerical reservoir simulator. The results were used in order to build a comprehensive spatio-temporal dataset, which includes all aspects of the reservoir model that is needed to train, calibrate, and validate the coupled SRM. From the parameters assimilated to form the comprehensive spatio-temporal dataset, Key Performance Indicators were identified and ranked. These KPIs helped to determine the dimensionality of the input space used to develop the SRMs (SRM W and SRMG).;Optimization may be identified by two focus areas. Building an efficient evaluation function and finding the quickest path to global minima. In this work, we focus on the efficiency of the evaluation function. The integrated SRM was built by coupling the two aforementioned SRMs. This SRM can be used to identify the optimal injection strategy (volume, rate, etc.) that would result in increased oil production while keeping an eye on the flood front