Deep learning-Based Quality Enhancement Algorithms for Background of Video

被引：0

作者：

Kobayashi, Kei ^{[1
]}

Katayama, Takafumi ^{[1
]}

Song, Tian ^{[1
]}

Shimamoto, Takashi ^{[1
]}

机构：

[1] Univ Tokushima, Grad Sch Sci & Technol Innovat, Tokushima, Japan

来源：

2022 37TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2022) | 2022年

关键词：

High-resolution video; Image quality enhancement; Background enhancement; Deep learning;

D O I：

10.1109/ITC-CSCC55581.2022.9895043

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this work, three quality enhancement algorithms are proposed to decrease the noise between the foreground and the background. The proposed algorithm is performed on the decoder side target to enhance the quality of the background with a higher quantization parameter to the same level of the foreground. The simulation results show that all three algorithms can improve the PSNR when using the proposed algorithms. The performance of these three algorithms is also discussed.

引用

页码：353 / 356

页数：4

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