1,720,962 research outputs found
Optimisation de la Complexité Basée sur l’Apprentissage Automatique Léger pour l’Encodage VVC en Temps Réel
No full textThe rapid growth of video-on-demand services, live-streaming platforms, and social media has led to video content dominating global internet traffic, accounting for over 82% of data. This surge in video consumption necessitates the development of more advanced video compression techniques. The VVC standard, introduced in July 2020, achieves a 50% reduction in bitrate compared to its predecessor High Efficiency Video Coding (HEVC) but significantly increases encoding complexity, up to 25.8 times. This thesis focuses on reducing VVC encoding complexity while preserving video quality. It presents a statistical analysis of the VVenC to identify areas for optimization, followed by the development of a machine learning-based technique utilizing Light Gradient Boosting Machine (LightGBM) classifiers to accelerate QTMTT partitioning in intra-coding. The research also extends these optimizations to inter-coding configurations. These proposed methods significantly reduce encoding time with minimal increases in bitrate, enhancing the feasibility of real-time VVC encoding for high-resolution video content.La croissance rapide des services de vidéo à la demande, des plateformes de streaming en direct et des réseaux sociaux a fait du contenu vidéo la forme dominante du trafic Internet mondial, représentant plus de 82 % des données. Cette explosion de la consommation de vidéos exige le développement de techniques de compression plus avancées.La norme VVC, introduite en juillet 2020, permet une réduction de 50 % du débit binaire par rapport à son prédécesseur HEVC, mais elle augmente considérablement la complexité de l’encodage, jusqu’à 25,8 fois.Cette thèse se concentre sur la réduction de la complexité de l’encodage VVC tout en préservant la qualité vidéo. Elle présente une analyse statistique de l’encodeur VVenC afin d’identifier des opportunités d’optimisation, suivie du développement d’une technique basée sur l’apprentissage automatique, utilisant des classificateurs LightGBM pour accélérer le partitionnement QTMTT dans le codage intra. La recherche étend également ces optimisations aux configurations de codage inter.Les méthodes proposées réduisent significativement le temps d’encodage avec une augmentation minimale du débit binaire, améliorant ainsi la faisabilité de l’encodage VVC en temps réel pour les vidéos haute résolution
Optimisation de la Complexité Basée sur l’Apprentissage Automatique Léger pour l’Encodage VVC en Temps Réel
No full textThe rapid growth of video-on-demand services, live-streaming platforms, and social media has led to video content dominating global internet traffic, accounting for over 82% of data. This surge in video consumption necessitates the development of more advanced video compression techniques. The VVC standard, introduced in July 2020, achieves a 50% reduction in bitrate compared to its predecessor High Efficiency Video Coding (HEVC) but significantly increases encoding complexity, up to 25.8 times. This thesis focuses on reducing VVC encoding complexity while preserving video quality. It presents a statistical analysis of the VVenC to identify areas for optimization, followed by the development of a machine learning-based technique utilizing Light Gradient Boosting Machine (LightGBM) classifiers to accelerate QTMTT partitioning in intra-coding. The research also extends these optimizations to inter-coding configurations. These proposed methods significantly reduce encoding time with minimal increases in bitrate, enhancing the feasibility of real-time VVC encoding for high-resolution video content.La croissance rapide des services de vidéo à la demande, des plateformes de streaming en direct et des réseaux sociaux a fait du contenu vidéo la forme dominante du trafic Internet mondial, représentant plus de 82 % des données. Cette explosion de la consommation de vidéos exige le développement de techniques de compression plus avancées.La norme VVC, introduite en juillet 2020, permet une réduction de 50 % du débit binaire par rapport à son prédécesseur HEVC, mais elle augmente considérablement la complexité de l’encodage, jusqu’à 25,8 fois.Cette thèse se concentre sur la réduction de la complexité de l’encodage VVC tout en préservant la qualité vidéo. Elle présente une analyse statistique de l’encodeur VVenC afin d’identifier des opportunités d’optimisation, suivie du développement d’une technique basée sur l’apprentissage automatique, utilisant des classificateurs LightGBM pour accélérer le partitionnement QTMTT dans le codage intra. La recherche étend également ces optimisations aux configurations de codage inter.Les méthodes proposées réduisent significativement le temps d’encodage avec une augmentation minimale du débit binaire, améliorant ainsi la faisabilité de l’encodage VVC en temps réel pour les vidéos haute résolution
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Optimisation de la Complexité Basée sur l’Apprentissage Automatique Léger pour l’Encodage VVC en Temps Réel
No full textThe rapid growth of video-on-demand services, live-streaming platforms, and social media has led to video content dominating global internet traffic, accounting for over 82% of data. This surge in video consumption necessitates the development of more advanced video compression techniques. The VVC standard, introduced in July 2020, achieves a 50% reduction in bitrate compared to its predecessor High Efficiency Video Coding (HEVC) but significantly increases encoding complexity, up to 25.8 times. This thesis focuses on reducing VVC encoding complexity while preserving video quality. It presents a statistical analysis of the VVenC to identify areas for optimization, followed by the development of a machine learning-based technique utilizing Light Gradient Boosting Machine (LightGBM) classifiers to accelerate QTMTT partitioning in intra-coding. The research also extends these optimizations to inter-coding configurations. These proposed methods significantly reduce encoding time with minimal increases in bitrate, enhancing the feasibility of real-time VVC encoding for high-resolution video content.La croissance rapide des services de vidéo à la demande, des plateformes de streaming en direct et des réseaux sociaux a fait du contenu vidéo la forme dominante du trafic Internet mondial, représentant plus de 82 % des données. Cette explosion de la consommation de vidéos exige le développement de techniques de compression plus avancées.La norme VVC, introduite en juillet 2020, permet une réduction de 50 % du débit binaire par rapport à son prédécesseur HEVC, mais elle augmente considérablement la complexité de l’encodage, jusqu’à 25,8 fois.Cette thèse se concentre sur la réduction de la complexité de l’encodage VVC tout en préservant la qualité vidéo. Elle présente une analyse statistique de l’encodeur VVenC afin d’identifier des opportunités d’optimisation, suivie du développement d’une technique basée sur l’apprentissage automatique, utilisant des classificateurs LightGBM pour accélérer le partitionnement QTMTT dans le codage intra. La recherche étend également ces optimisations aux configurations de codage inter.Les méthodes proposées réduisent significativement le temps d’encodage avec une augmentation minimale du débit binaire, améliorant ainsi la faisabilité de l’encodage VVC en temps réel pour les vidéos haute résolution
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Fully self-supervised physics-aware holographic depth estimation
No full textInternational audienceAutofocusing is a well-studied topic in holography, with a wide range of proposed methods, from mathematical models to more recent learning-based approaches. However, there is no consensus on a universal method that can autofocus an input hologram independently of the experimental setup. Minor changes in factors such as wavelength, pixel pitch, or hologram resolution can drastically impact the autofocus outcome. In this paper, we introduce a universal methodology that adheres to the general framework of holographic autofocusing while eliminating the need for manual hyperparameter tuning and offering robust adaptability to diverse input data. Our approach autonomously extracts optimal numerical reconstruction distances, performs volumetric rendering of the hologram, and estimates the underlying scene geometry to achieve precise autofocusing. To ensure accuracy, the generated depth estimates are constrained by matching the ground-truth values through an iterative hologram regeneration process. Our method demonstrates superior robustness and generalization on both synthetic computer-generated holograms and optically acquired on-axis phase-shifting holograms, marking a significant step toward universal autofocusing in holography
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