SVM-Based Fast CU Partition Decision Algorithm for VVC Intra Coding

Zhao, Jinchao; Wu, Aobo; Zhang, Qiuwen

doi:10.3390/electronics11142147

Cited by 11 publications

(18 citation statements)

References 31 publications

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“…To meet this demand, researchers are committed to designing efficient and low-complexity video encoders. Although there has been some research work on reducing the computational complexity of VVC encoders, most of the research has focused on accelerating early decision making in the partitioning process [15][16][17][18][19][20][21]. Reference [15] proposed a fast partitioning algorithm for intra and inter-frame encoding.…”

Section: Related Workmentioning

confidence: 99%

“…Reference [18] studied a fast motion estimation algorithm for the early termination of partial blocks in the CU, using skip mode for the CU that does not require affine changes. In reference [19], Zhao et al extracted the standard deviation and edge ratio to accelerate the division of the CU. The CU split information and the time position of the encoded frame are used for low-complexity encoders [20].…”

Section: Related Workmentioning

confidence: 99%

“…However, the computational complexity of AME is high, and the motion estimation algorithm in the interprediction module takes up a long encoding time [14]. To achieve better encoding gain while reducing its computational complexity, many researchers have attempted to reduce its computational complexity in traditional affine motion estimation modules [15][16][17][18][19][20][21]. However, there is still little work in the inter-frame prediction affine motion estimation module of VVC and there is still a large optimization space.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

et al. 2023

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In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

et al. 2023

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“…In addition, they proposed the concept of stage grid maps by dividing the entire partition into four stages to represent the structured output and consequently predict all partition information of the 32 × 32-sized CUs and their sub-CUs as the model outputs. 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.…”

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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“…To reduce the coding complexity of the intra VVC encoder, many fast CU decision methods [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] have proposed the acceleration of intra QTMT partition. For example, in [ 19 ], the authors propose a directional gradient-based early termination CU partition method in which four directional gradients (horizontal, vertical, 45, and 135) of CUs are extracted separately to represent the relationship between the optimal CU partition and the texture characteristics of the CU.…”

Section: Introductionmentioning

confidence: 99%

Temporal Prediction Model-Based Fast Inter CU Partition for Versatile Video Coding

Luo

Zhu

2022

Sensors

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Versatile video coding (VVC) adopts an advanced quad-tree plus multi-type tree (QTMT) coding structure to obtain higher compression efficiency, but it comes at the cost of a considerable increase in coding complexity. To effectively reduce the coding complexity of the QTMT-based coding unit (CU) partition, we propose a fast inter CU partition method based on a temporal prediction model, which includes early termination QTMT partition and early skipping multi-type tree (MT) partition. Firstly, according to the position of the current CU, we extract the optimal CU partition information of the position corresponding to the previously coded frames. We then establish a temporal prediction model based on temporal CU partition information to predict the current CU partition. Finally, to reduce the cumulative of errors of the temporal prediction model, we further extract the motion vector difference (MVD) of the CU to determine whether the QTMT partition can be terminated early. The experimental results show that the proposed method can reduce the inter coding complexity of VVC by 23.19% on average, while the Bjontegaard delta bit rate (BDBR) is only increased by 0.97% on average under the Random Access (RA) configuration.

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SVM-Based Fast CU Partition Decision Algorithm for VVC Intra Coding

Cited by 11 publications

References 31 publications

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

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

Temporal Prediction Model-Based Fast Inter CU Partition for Versatile Video Coding

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