This book describes and analyzes in detail the encoding effort and the encoding tool usage applied to 3D-HEVC depth map coding. Based on the analyzed information, the authors introduce efficient algorithms for accelerating the available encoding tools. The contributions discussed in this book include four algorithms for reducing intra-frame encoding effort and three algorithms for reducing inter-frame encoding effort. The presented results demonstrate several levels of encoding effort reduction with different impacts in the encoding efficiency, surpassing state-of-the-art solutions by more than 50% the encoding effort with only 0.3% encoding efficiency loss.

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, 360 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 describes and analyzes in detail the encoding effort and the encoding tool usage applied to 3D-HEVC depth map coding. Based on the analyzed information, the authors introduce efficient algorithms for accelerating the available encoding tools. The contributions discussed in this book include four algorithms for reducing intra-frame encoding effort and three algorithms for reducing inter-frame encoding effort. The presented results demonstrate several levels of encoding effort reduction with different impacts in the encoding efficiency, surpassing state-of-the-art solutions by more than 50% the encoding effort with only 0.3% encoding efficiency loss.