Deep Multi-Task Learning Based Fast Intra-Mode Decision for Versatile Video Coding

Liu, Zheng; Li, Tianyi; Chen, Ying; Wei, Kaijin; Xu, Mai; Qi, Honggang

doi:10.1109/tcsvt.2023.3262733

Cited by 13 publications

(2 citation statements)

References 60 publications

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“…The method has huge execution complexity and does not produce much accurate prediction. Liu [21] introduced multiple task intra-mode decision network (MID-Net) method for efficiently predicting the suitable angular modes in versatile video coding (VVC). The introduced method involved rough mode decision (RMD) and candidate mode list (CML).…”

Section: Literature Reviewmentioning

confidence: 99%

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

2024

IJIES

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High-efficiency video coding (HEVC) stands as one of the most extensively employed video coding standards, with the primary encoding challenge revolving around the searching process. To minimize coding complexity of HEVC block partition, a new algorithm is proposed for supporting the partition architecture of coding tree unit (CTU) in intra-coding. In this research, problem of partition architecture decisions of CTU is resolved through two stages. In first phase, the bagged tree method is evaluated for predicting CTU splitting, and in the second phase, the partition issue of 32 × 32 size CU is given as 17 outcome classification. To attain a huge accuracy of prediction, a residual attention-based long short-term memory (LSTM) with parametric rectified linear unit (AT-LSTM-PReLU) is proposed. The proposed method produces a structure of partition quad-tree of CTU that makes multiple decisions at variant depth levels. The performance of proposed method is estimated with a dataset gathered from the joint collaborative team and video coding (JCT-VC). The proposed approach achieved a remarkable bit rate improvement of 3.72%, a significant ∆T enhancement of 97.28%, and minimized video quality degradation by just -0.05dB. These results outperform other deep learning methods like convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

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Section: Literature Reviewmentioning

confidence: 99%

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

2024

IJIES

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“…Considering that the machine learning algorithm has shown its superiority in many aspects in the field of video processing [7][8][9][10][11][12][13] , this paper introduces a Fast Modes Decision (FMD) method based on machine learning. However, there are the following problems: First of all, the processing of projection videos in V-PCC differs from the processing of traditional videos in HEVC.…”

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confidence: 99%

Portable Perceptron Network-Based Fast Mode Decision for Video-Based Point Cloud Compression

Que,

2023

CAAI Artificial Intelligence Research

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In Video-based Point Cloud Compression (V-PCC), 2D videos to be encoded are generated by 3D point cloud projection, and compressed by High Efficiency Video Coding (HEVC). In the process of 2D video compression, the best mode of Coding Unit (CU) is searched by brute-force strategy, which greatly increases the complexity of the encoding process. To address this issue, we first propose a simple and effective Portable Perceptron Network (PPN)-based fast mode decision method for V-PCC under Random Access (RA) configuration. Second, we extract seven simple hand-extracted features for input into the PPN network. Third, we design an adaptive loss function, which can calculate the loss by allocating different weights according to different Rate-Distortion (RD) costs, to train our PPN network. Finally, experimental results show that the proposed method can save encoding complexity of 43.13% with almost no encoding efficiency loss under RA configuration, which is superior to the state-of-the-art methods. The source code is available at https://github.com/Mesks/PPNforV-PCC.

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Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

Chen,

Huang,

Tao

et al. 2024

J Real-Time Image Proc

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Deep Multi-Task Learning Based Fast Intra-Mode Decision for Versatile Video Coding

Cited by 13 publications

References 60 publications

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

Portable Perceptron Network-Based Fast Mode Decision for Video-Based Point Cloud Compression

Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

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