Convolutional Neural Network Based Fast Intra Mode Prediction for H.266/FVC Video Coding

Lin, Ting-Lan; Liang, Kai Ming; Huang, Jing-Ya; Tu, Yu-Liang; Chang, Pao-Chi

doi:10.1109/dcc47342.2020.00075

Cited by 3 publications

(2 citation statements)

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“…Zhao et al [17] proposed a support vector machine (SVM)-based fast CU partition decision algorithm by analyzing the ratio of the split modes of CUs of different sizes to effectively reduce the coding complexity of VVC. Jin et al [18] proposed a CNN-based fast QTBT partitioning method to predict the depth range of the QTBT partition for 32 × 32 blocks based on the inherent texture richness of the image rather than judging the split at each depth level. Pan et al [19] proposed an early termination of the QTMT-based block partition process using a multi-information fusion CNN (MFCNN) model.…”

Section: Related Workmentioning

confidence: 99%

Fast Mode Decision Method of Multiple Weighted Bi-Predictions Using Lightweight Multilayer Perceptron in Versatile Video Coding

Lee

Jun

2023

Electronics

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Versatile Video Coding (VVC), the state-of-the-art video coding standard, was developed by the Joint Video Experts Team (JVET) of ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) in 2020. Although VVC can provide powerful coding performance, it requires tremendous computational complexity to determine the optimal mode decision during the encoding process. In particular, VVC adopted the bi-prediction with CU-level weight (BCW) as one of the new tools, which enhanced the coding efficiency of conventional bi-prediction by assigning different weights to the two prediction blocks in the process of inter prediction. In this study, we investigate the statistical characteristics of input features that exhibit a correlation with the BCW and define four useful types of categories to facilitate the inter prediction of VVC. With the investigated input features, a lightweight neural network with multilayer perceptron (MLP) architecture is designed to provide high accuracy and low complexity. We propose a fast BCW mode decision method with a lightweight MLP to reduce the computational complexity of the weighted multiple bi-prediction in the VVC encoder. The experimental results show that the proposed method significantly reduced the BCW encoding complexity by up to 33% with unnoticeable coding loss, compared to the VVC test model (VTM) under the random-access (RA) configuration.

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Section: Related Workmentioning

confidence: 99%

Fast Mode Decision Method of Multiple Weighted Bi-Predictions Using Lightweight Multilayer Perceptron in Versatile Video Coding

Lee

Jun

2023

Electronics

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“…Li et al [33] addressed a deep learning approach to predict QTMT-based CU partition to accelerate the encoding process in the intra prediction of VVC. Lin et al [34] proposed a CNN-based intra mode decision method with two convolutional layers and a fully connected layer. Zhao et al [35] developed an adaptive CU split decision method with the deep learning and multi-featured fusion.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

Lee¹,

Jun²,

Park³

et al. 2022

Computers, Materials &Amp; Continua

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As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT) split and Ternary Tree (TT) split. However, VVC encoder requires heavy computational complexity due to the excessive Ratedistortion Optimization (RDO) processes used to determine the optimal MTT block mode. In this paper, we propose a fast MTT decision method with two Lightweight Neural Networks (LNNs) using Multi-layer Perceptron (MLP), which are applied to determine the early termination of the TT split within the encoding process. Experimental results show that the proposed method significantly reduced the encoding complexity up to 26% with unnoticeable coding loss compared to the VVC Test Model (VTM).

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Ultra-Lightweight CNN Based Fast Intra Prediction for VVC Screen Content Coding

Tang

Chen

et al. 2023

2023 IEEE International Symposium on Circuits and Systems (ISCAS)

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Convolutional Neural Network Based Fast Intra Mode Prediction for H.266/FVC Video Coding

Cited by 3 publications

References 0 publications

Fast Mode Decision Method of Multiple Weighted Bi-Predictions Using Lightweight Multilayer Perceptron in Versatile Video Coding

Fast Mode Decision Method of Multiple Weighted Bi-Predictions Using Lightweight Multilayer Perceptron in Versatile Video Coding

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

Ultra-Lightweight CNN Based Fast Intra Prediction for VVC Screen Content Coding

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