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Versatile Video Coding (VVC) : Machine Learning and Heuristics / by Mário Saldanha, Gustavo Sanchez, César Marcon, Luciano Agostini

By: Saldanha, Mário, autor
Contributor(s): Sánchez, Gustavo, autor | Marcon, César, autor | Agostini, Luciano, autor
Material type: materialTypeLabelE-bookSeries: (Synthesis Lectures on Engineering Science and Technology, 2690-0327).Publisher: Cham : Springer International Publishing, 2022Edition: 1st edition 2022.Description: 1 recurso en línea (XI, 123 páginas) : 50 ilustraciones, 45 ilustraciones en blanco y negro.ISBN: 9783031116407.Subject: Vídeo digital | Aprendizaje automático | Codificación, Teoría de laOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction -- Versatile Video Coding -- VVC Intra-Frame Prediction -- State-of-the-Art Overview -- Performance Analysis of VVC Intra-Frame Prediction -- Heuristic Based Fast Multi-Type Tree Decision Scheme for Luminance -- Light Gradient Boosting Machine Configurable Fast Block Partitioning for Luminance -- Learning-Based Fast Decision for Intra-Frame Prediction Mode Selection for Luminance -- Fast Intra-Frame Prediction Transform for Luminance Using Decision Trees -- Heuristic Based Fast Block Partitioning Scheme for Chrominance -- Conclusions.
Summary: This book discusses the Versatile Video Coding (VVC), the ISO and ITU state-of-the-art video coding standard. VVC reaches a compression efficiency significantly higher than its predecessor standard (HEVC) and it has a high versatility for efficient use in a broad range of applications and different types of video content, including Ultra-High Definition (UHD), High-Dynamic Range (HDR), screen content, 360º videos, and resolution adaptivity. The authors introduce the novel VVC tools for block partitioning, intra-frame and inter-frames predictions, transforms, quantization, entropy coding, and in-loop filtering. The authors also present some solutions exploring VVC encoding behavior at different levels to accelerate the intra-frame prediction, applying statistical-based heuristics and machine learning (ML) techniques. This book includes: A high-level description of the VVC novel encoding tools; A detailed description of the VVC intra-frame prediction; A deep statistical assessment of the VVC intra-frame prediction behavior; Five algorithms to reduce the VVC intra-frame prediction encoding effort.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería TK6680.5 2022 EB (Browse shelf(Opens below)) Acceso electrónico eBook.01112108
Total holds: 0

Introduction -- Versatile Video Coding -- VVC Intra-Frame Prediction -- State-of-the-Art Overview -- Performance Analysis of VVC Intra-Frame Prediction -- Heuristic Based Fast Multi-Type Tree Decision Scheme for Luminance -- Light Gradient Boosting Machine Configurable Fast Block Partitioning for Luminance -- Learning-Based Fast Decision for Intra-Frame Prediction Mode Selection for Luminance -- Fast Intra-Frame Prediction Transform for Luminance Using Decision Trees -- Heuristic Based Fast Block Partitioning Scheme for Chrominance -- Conclusions.

This book discusses the Versatile Video Coding (VVC), the ISO and ITU state-of-the-art video coding standard. VVC reaches a compression efficiency significantly higher than its predecessor standard (HEVC) and it has a high versatility for efficient use in a broad range of applications and different types of video content, including Ultra-High Definition (UHD), High-Dynamic Range (HDR), screen content, 360º videos, and resolution adaptivity. The authors introduce the novel VVC tools for block partitioning, intra-frame and inter-frames predictions, transforms, quantization, entropy coding, and in-loop filtering. The authors also present some solutions exploring VVC encoding behavior at different levels to accelerate the intra-frame prediction, applying statistical-based heuristics and machine learning (ML) techniques. This book includes: A high-level description of the VVC novel encoding tools; A detailed description of the VVC intra-frame prediction; A deep statistical assessment of the VVC intra-frame prediction behavior; Five algorithms to reduce the VVC intra-frame prediction encoding effort.

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