Fully Connected Neural Network Based Lifting Scheme with Adaptive Split for Image Compression

被引：0
|
作者：
Hsin, Hsi-Chin [1 ]
Su, Chien-Kun [2 ]
Yang, Cheng-Ying [3 ]
机构：
[1] Natl United Univ, Dept Comp Sci & Informat Engn, 1 Lienda, Miaoli, Taiwan
[2] Chung Hua Univ, Dept Elect Engn, Hsinchu, Taiwan
[3] Univ Taipei, Dept Comp Sci, Taipei, Taiwan
来源：
2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 | 2024年
关键词：
wavelet transform; lifting scheme; fully connected neural network; image compression;
D O I：
10.1109/ICCE-Taiwan62264.2024.10674265
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：
Wavelet transform (WT) provides an efficient multi-resolution representation with many desirable properties, which are beneficial for effective compression. To improve the computational efficiency, the lifting scheme (LS) based WT has been widely used in many applications. Recently, the fully connected neural network (FCNN) based LS known as the next generation of WT has been used for learned image compression. This paper presents an adaptive scheme to improve the performance of FCNN-LS by replacing its fixed split unit with an adaptive one.
引用
收藏
页码：529 / 530
页数：2
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