A Machine Learning Approach for Fast CU Partitioning and Time Complexity Reduction in Video Coding

Islam, Md. Zahirul; Ahmed, Boshir

doi:10.1109/eict54103.2021.9733593

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…To attain optimal partition results, the rate-distortion cost of all splitting modes is calculated at each partition depth layer in VVC, and the best partition result is obtained based on the partition type of the minimum RD cost. According to the research, the process of QTMT-based CU partitioning in intra-encoding accounts for 93.84% of the total coding time, while the sum of the other processes accounts for only 6.16% of the time [7], which shows that flexible CU partitioning makes a significant contribution to improving the encoding performance of VVC but also increases the total coding time and greatly increases the coding complexity. This limits the application of VVC in the mobile terminal and other application scenarios with high requirements for real-time video coding [8].…”

Section: Introductionmentioning

confidence: 99%

FSVM- and DAG-SVM-Based Fast CU-Partitioning Algorithm for VVC Intra-Coding

Wang

Zhang

2023

Symmetry

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H.266/VVC introduces the QTMT partitioning structure, building upon the foundation laid by H.265/HEVC, which makes the partitioning more diverse and flexible but also brings huge coding complexity. To better address the problem, we propose a fast CU decision algorithm based on FSVMs and DAG-SVMs to reduce encoding time. The algorithm divides the CU-partitioning process into two stages and symmetrically extracts some of the same CU features. Firstly, CU is input into the trained FSVM model, extracting the standard deviation, directional complexity, and content difference complexity of the CUs, and it uses these features to make a judgment on whether to terminate the partitioning early. Then, the determination of the partition type of CU is regarded as a multi-classification problem, and a DAG-SVM classifier is used to classify it. The extracted features serve as input to the classifier, which predicts the partition type of the CU and thereby prevents unnecessary partitioning. The results of the experiment indicate that compared with the reference software VTM10.0 anchoring algorithm, the algorithm can save 49.38%~58.04% of coding time, and BDBR only increases by 0.76%~1.37%. The video quality and encoding performance are guaranteed while the encoding complexity is effectively reduced.

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Section: Introductionmentioning

confidence: 99%