IVMMMay 19

Partition Tree Search Acceleration for VVC: Survey and Evaluation with VTM Evolution

arXiv:2605.215260.6
Predicted impact top 92% in IV · last 90 daysOriginality Synthesis-oriented
AI Analysis

For researchers and developers working on VVC encoding optimization, this survey provides a critical evaluation of acceleration methods and their adaptation to VTM changes.

The paper surveys and evaluates state-of-the-art partitioning acceleration techniques for VVC, analyzing their evolution across VTM versions and highlighting challenges in balancing encoding complexity and compression efficiency.

The Versatile Video Coding (VVC) standard, introduced in 2020, offers 40-50% bitrate savings for equivalent visual quality of reconstructed videos over its predecessor, High Efficiency Video Coding (HEVC), at the cost of significantly increased encoding complexity. This growth in encoding complexity is mainly due to the addition of the Quad Tree Multi Type Tree (QTMTT) partitioning structure, which increases the split combinatorial complexity. This paper presents a critical evaluation of state-of-the-art (SOTA) partitioning acceleration techniques designed to reduce the complexity of the partitioning search in VVC. Particular attention is given to how these methods have evolved alongside successive versions of the VVC Test Model (VTM), which serves as the reference software for benchmarking coding tools. These techniques are analyzed in the context of their adaptation to internal changes in VTM, such as updated heuristics for fast partitioning decisions. The study also highlights the challenges involved in improving the trade-off between encoding complexity and compression efficiency. This challenge becomes more pronounced when evaluating methods across diverse VTM configurations and multiple software versions.

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