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

By: Saldanha, Mário [author.].
Contributor(s): Sanchez, Gustavo [author.] | Marcon, César [author.] | Agostini, Luciano [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Engineering, Science, and Technology: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2022Edition: 1st ed. 2022.Description: XI, 123 p. 50 illus., 45 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031116407.Subject(s): Electronic circuits | Signal processing | Embedded computer systems | Electronic Circuits and Systems | Signal, Speech and Image Processing | Embedded SystemsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.3815 Online resources: Click here to access online
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.
In: Springer Nature eBookSummary: 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 ofthe 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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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 ofthe 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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