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. 2023 Jul 7;23(13):6244.
doi: 10.3390/s23136244.

A Fast Algorithm for Intra-Frame Versatile Video Coding Based on Edge Features

Shuai Zhao  1 , Xiwu Shang  1 , Guozhong Wang  1 , Haiwu Zhao  1
Affiliations

A Fast Algorithm for Intra-Frame Versatile Video Coding Based on Edge Features

Shuai Zhao et al. Sensors (Basel). .

Abstract

Versatile Video Coding (VVC) introduces many new coding technologies, such as quadtree with nested multi-type tree (QTMT), which greatly improves the efficiency of VVC coding. However, its computational complexity is higher, which affects the application of VVC in real-time scenarios. Aiming to solve the problem of the high complexity of VVC intra coding, we propose a low-complexity partition algorithm based on edge features. Firstly, the Laplacian of Gaussian (LOG) operator was used to extract the edges in the coding frame, and the edges were divided into vertical and horizontal edges. Then, the coding unit (CU) was equally divided into four sub-blocks in the horizontal and vertical directions to calculate the feature values of the horizontal and vertical edges, respectively. Based on the feature values, we skipped unnecessary partition patterns in advance. Finally, for the CUs without edges, we decided to terminate the partition process according to the depth information of neighboring CUs. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 54.08% of the encoding time on average, and the BDBR (Bjøntegaard delta bit rate) only increases by 1.61%.

Keywords: CU partition; edge direction; fast algorithm; versatile video coding (VVC).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of the CU partition structure in VVC. (a) example of the partition structure obtained after recursive traversal; (b) the corresponding tree structure.
Figure 2
Figure 2
(a) The partition result for Johnny under QP = 32. (b) The edge extraction for Johnny with σ = 0.1. (c) The edge extraction for Johnny with σ = 2.1.
Figure 2
Figure 2
(a) The partition result for Johnny under QP = 32. (b) The edge extraction for Johnny with σ = 0.1. (c) The edge extraction for Johnny with σ = 2.1.
Figure 3
Figure 3
Depth difference ratios under different σ values.
Figure 4
Figure 4
Image differentiation effect. (a) Original edge map. (b) Horizontal edges. (c) Vertical edges.
Figure 5
Figure 5
Adjacent coding unit (CU)s.
Figure 6
Figure 6
A flowchart of the proposed algorithm.
Figure 7
Figure 7
The comparison of RD performance between the proposed algorithm and the original encoder. (a) RD performance of BasketballDrill. (b) RD performance of BQTerrace.
See this image and copyright information in PMC

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