基于决策树的多视觉任务自适应压缩算法

doi:10.12305/j.issn.1001-506X.2025.08.31

Abstract

Abstract:

To address high transmission costs and the computational burden of multi-visual tasks at the decoding end, an adaptive scalable video coding （ASVC） transmission framework is proposed. The framework divides video into semantic and background layers, transmitting these separately. Additionally, an adaptive compression algorithm is proposed, utilizing a C4.5 decision tree model to analyze the network environment and make compression decisions. Optical flow analysis is employed to retain frames with significant changes, while an interpolation mechanism ensures image smoothness. Simulation results demonstrate that the ASVC method achieves higher recognition accuracy, improved video quality, and transmission efficiency across various bitrate environments.

Key words: adaptive compression algorithm, C4.5 decision tree, optical flow detection, multi-visual task

CLC Number:

TN 929.5

Xiaohui LI, Wen YANG, Siting LYU, Liang MAO. Decision tree-based adaptive compression algorithm for multi-visual tasks[J]. Systems Engineering and Electronics, 2025, 47(8): 2737-2743.

Figures/Tables 10

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References 30

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算法	码率/Mbps	丢包率	mAP
H.264	1.2	0.58	0.22
	0.96	0.48	0.24
	0.72	0.31	0.35
ASVC	1.2	0	0.66
	0.96	0	0.61
	0.72	0	0.56

算法	带宽/Mbps	丢包率	VMAF	SSIM	PSNR
H.264	1	0.15	0.42	0.81	0.45
	0.75	0.36	0.34	0.78	0.42
	0.5	0.57	0.22	0.75	0.38
ASVC	1	0	0.7	0.93	0.56
	0.75	0	0.71	0.93	0.57
	0.5	0	0.68	0.91	0.55

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Decision tree-based adaptive compression algorithm for multi-visual tasks

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