High compression rate, based on the RLS adaptive algorithm in progressive image transmission

被引:0
作者
Nooshin Abdollahi
Kamal Shahtalebi
Mohamad Farzan Sabahi
机构
[1] University of Isfahan,Department of Electrical Engineering, Faculty of Engineering
[2] University of Toronto,Institute of Biomedical Engineering
来源
Signal, Image and Video Processing | 2021年 / 15卷
关键词
Progressive image transmission; RLS algorithm; Linear regression;
D O I
暂无
中图分类号
学科分类号
摘要
The purpose of this paper is to develop a novel method based on recursive least squares (RLS) adaptive algorithm for progressive image transmission (PIT). The image is divided into non-overlapping blocks. Having an agreed vector sequence between the transmitter and the receiver, each block is related to a regressive model. Meanwhile, at the transmitter the blocks are estimated using the RLS algorithm. The high correlation between error vectors, regarding to the RLS execution, causes a very high compression rate in their transmission. The error vectors at the receiver are used to run the RLS algorithm and to estimate the image in a same manner. The method is easy to implement with a low computational complexity and achieves high quality, compared to other well-known methods. In comparison with its counterparts, simulation results show how efficient the proposed method is.
引用
收藏
页码:835 / 842
页数:7
相关论文
共 21 条
[1]  
Liu L(2014)A novel real-time and progressive secret image sharing with flexible shadows based on compressive sensing Signal Process. Image Commun. 29 128-134
[2]  
Huang H-C(2016)Ownership protection for progressive image transmission with reversible data hiding and visual secret sharing Optik-Int. J. Light Electron Opt. 127 5950-5960
[3]  
Yuh-Yih L(2015)Progressive quality coding for compression of medical images in telemedicine Int. J. Telemed. Clin. Pract. 1 125-140
[4]  
Lin J(2017)Multi-resolution lossless image compression for progressive transmission and multiple decoding using an enhanced edge adaptive hierarchical interpolation KSII Trans. Internet Inf. Syst. 11 12-1503
[5]  
Moorthi M(2014)Progressive image denoising through hybrid graph laplacian regularization: a unified framework IEEE Trans. Image Process. 23 1491-1621
[6]  
Amutha R(2018)Efficient and robust image coding and transmission based on scrambled block compressive sensing IEEE Trans. Multimed. 20 1610-438
[7]  
Biadgie Y(2001)New interleaved hierarchical interpolation with median-based interpolators for progressive image transmission Signal Process. 81 431-1844
[8]  
Min-sung KK-AS(2005)A multi-segment image coding and transmission scheme Signal Process. 85 1827-1342
[9]  
Liu X(1979)Image compression using block truncation coding IEEE Trans. Commun. 27 1335-110
[10]  
Chen Z(2010)High efficiency ordered dither block truncation coding with dither array LUT and its scalable coding application Digit. Signal Process. 20 97-1341