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Partition Map Prediction for Fast Block Partitioning in VVC Intra-Frame Coding.

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    We developed a fast block partitioning method for Versatile Video Coding (VVC) intra-frame encoding using a convolutional neural network (CNN). This approach accelerates encoding significantly with minimal loss in compression efficiency.

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    Area of Science:

    • Video Compression Technologies
    • Machine Learning for Video Processing
    • Digital Signal Processing

    Background:

    • Versatile Video Coding (VVC) offers enhanced compression efficiency through flexible block partitioning (QTMTT).
    • The partition search (PS) process in VVC is computationally intensive and complex, posing challenges for real-time applications and hardware implementation.
    • Existing fast block partitioning methods for VVC lack optimal trade-offs between encoding speed and compression efficiency.

    Purpose of the Study:

    • To propose a novel partition map prediction method for accelerating VVC intra-frame encoding.
    • To develop a Convolutional Neural Network (CNN) based approach for predicting optimal block partitioning structures.
    • To achieve adjustable encoding acceleration with minimal impact on compression quality.

    Main Methods:

    • Representing QTMTT block partitioning structures using a partition map (QT depth map, MTT depth maps, MTT direction maps).
    • Utilizing a novel CNN architecture (Down-Up-CNN) to predict the partition map directly from image pixels, emulating the recursive nature of PS.
    • Implementing a post-processing algorithm to ensure standard-compliant partitioning and enable hybrid PS/prediction approaches.

    Main Results:

    • Achieved encoding acceleration ranging from 1.61× to 8.64× on the VTM-10.0 intra-frame encoder.
    • Demonstrated a favorable trade-off between speed and compression efficiency, with a 3.89× acceleration resulting in only 2.77% BD-rate loss.
    • The proposed method offers adjustable acceleration by controlling the extent to which the partition search is performed.

    Conclusions:

    • The proposed CNN-based partition map prediction significantly accelerates VVC intra-frame encoding.
    • This method provides a flexible and efficient solution for optimizing the trade-off between encoding speed and compression quality.
    • The approach is adaptable for hardware implementation and offers better performance compared to existing fast partitioning techniques.