Fast CU Decision Algorithm Based on CNN and Decision Trees for VVC

Li, Hongchan; Zhang, Peng; Jin, Beihong; Zhang, Qiuwen

doi:10.3390/electronics12143053

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Ref. [15] used a fast decision algorithm based on the DenseNet network and decision tree classifier to reduce VVC encoding time.…”

Section: Related Workmentioning

confidence: 99%

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Wang,

Zhang

2023

Electronics

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The new generation of 3D video is an international frontier research hotspot. However, the large amount of data and high complexity are core problems to be solved urgently in 3D video coding. The latest generation of video coding standard versatile video coding (VVC) adopts the quad-tree with nested multi-type tree (QTMT) partition structure, and the coding efficiency is much higher than other coding standards. However, the current research work undertaken for VVC is less for 3D video. In light of this context, we propose a fast coding unit (CU) decision algorithm based on the gray level co-occurrence matrix (GLCM) and Extra trees for the characteristics of the depth map in 3D video. In the first stage, we introduce an edge detection algorithm using GLCM to classify the CU in the depth map into smooth and complex edge blocks based on the extracted features. Subsequently, the extracted features from the CUs, classified as complex edge blocks in the first stage, are fed into the constructed Extra trees model to make a fast decision on the partition type of that CU and avoid calculating unnecessary rate-distortion cost. Experimental results show that the overall algorithm can effectively reduce the coding time by 36.27–51.98%, while the Bjøntegaard delta bit rate (BDBR) is only increased by 0.24% on average which is negligible, all reflecting the superior performance of our method. Moreover, our algorithm can effectively ensure video quality while saving much encoding time compared with other algorithms.

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“…Ref. [15] used a fast decision algorithm based on the DenseNet network and decision tree classifier to reduce VVC encoding time.…”

Section: Related Workmentioning

confidence: 99%

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Wang,

Zhang

2023

Electronics

Self Cite

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Early quadtree with nested multitype tree partitioning algorithm based on convolution neural network for the versatile video coding standard

Abdallah,

Jdidia,

Belghith

et al. 2024

J. Electron. Imag.

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The Joint Video Experts Team has recently finalized the versatile video coding (VVC) standard, which incorporates various advanced encoding tools. These tools ensure great enhancements in the coding efficiency, leading to a bitrate reduction up to 50% when compared to the previous standard, high-efficiency video coding. However, this enhancement comes at the expense of high computational complexity. Within this context, we address the new quadtree (QT) with nested multitype tree partition block in VVC for all-intra configuration. In fact, we propose a fast intra-coding unit (CU) partition algorithm using various convolution neural network (CNN) classifiers to directly predict the partition mode, skip unnecessary split modes, and early exit the partitioning process. The proposed approach first predicts the QT depth at a CU of size 64 × 64 by the corresponding CNN classifier. Then four CNN classifiers are applied to predict the partition decision tree at a CU of size 32 × 32 using multithreshold values and ignore the rate-distortion optimization process to speed up the partition coding time. Thus the developed method is implemented on the reference software VTM 16.2 and tested for different video sequences. The experimental results confirm that the proposed solution achieves an encoding time reduction of about 46% in average, reaching up to 67.3% with an acceptable increase in bitrate and an unsignificant decrease in quality.

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A Review of Fast Decision Algorithms for CU Partitioning Patterns for VVC Standard

胡

2024

SEA

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Fast CU Decision Algorithm Based on CNN and Decision Trees for VVC

Cited by 3 publications

References 36 publications

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

Early quadtree with nested multitype tree partitioning algorithm based on convolution neural network for the versatile video coding standard

A Review of Fast Decision Algorithms for CU Partitioning Patterns for VVC Standard

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