A Fast CU Partition Decision Strategy for AVS3 Intra Coding

Chen, Changxin; Luo, Xinjie; Yan, Yunyao; Xiang, Guoqing; Zhang, Peng; Huang, Xiaofeng; Yan, Wei

doi:10.1109/icce53296.2022.9730138

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…For statistic-based methods, [18,19] and [20] used features that depict the texture and character of CU to skip unnecessary steps of CU partition. The results of these methods may not be so competitive nowadays, while features mentioned in the articles have been widely used in subsequent researches.…”

Section: Fast Algorithms For Vvcmentioning

confidence: 99%

QTMT‐LNN: A fast intra CU partition using lightweight neural network for 360‐degree video coding on VVC

Peng

2022

IET Image Processing

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In the newest generation of video coding standard, Versatile Video Coding (VVC), a new technique called Quad Tree with nested Multi‐type Tree (QTMT) structure is introduced. QTMT significantly improves the coding efficiency, but the improvement in compression performance comes at the cost of drastically increased complexity. This paper proposes a fast intra partition algorithm using Lightweight Neural Network (LNN) to skip QTMT partition steps which are unlikely to be chosen as the best split modes. Specifically, five LNNs (for five QTMT split modes) are trained, using features that consider the characteristic of 360‐degree videos. The experimental results demonstrate that the proposed QTMT‐LNN can reduce the encoding time from 52.28% to 72.17% on average, with coding efficiency losses ranging from 0.71% to 1.78%. Compared to other fast algorithms for intra coding unit (CU) partition, the method outperforms the related works in terms of Bjontegaard delta bit‐rate (BDBR) and encoding time reduction.

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Section: Fast Algorithms For Vvcmentioning

confidence: 99%