A modified SPIHT algorithm for image coding with a joint MSE and classification distortion measure

2006 ◽  
Vol 15 (3) ◽  
pp. 713-725 ◽  
Author(s):  
Shaorong Chang ◽  
L. Carin
2009 ◽  
Vol 19 (2) ◽  
pp. 220-228 ◽  
Author(s):  
Tahar Brahimi ◽  
Ali Melit ◽  
Fouad Khelifi
Keyword(s):  

Author(s):  
CHENG-YOU WANG ◽  
ZHENG-XIN HOU ◽  
AI-PING YANG

In recent years, image coding based on wavelet transform has made rapid progress. In this paper, quincunx lifting scheme in wavelet transform is introduced and all phase interpolation filter banks which can be used in the lifting scheme for prediction and update are designed. Based on the basic idea of set partitioning in hierarchical trees (SPIHT) algorithm, the binary tree image coding algorithm is proposed. Just like SPIHT, the encoding algorithms can be stopped at any compressed file size or let run until the compressed file is a representation of a nearly lossless image. The experimental results on test images show that compared with SPIHT algorithm, the PSNRs of the proposed algorithm are superior by about 0.5 dB at the same bit rates and the subjective quality of reconstructed images is also better.


Author(s):  
Thomas Andre ◽  
Marc Antonini ◽  
Michel Barlaud ◽  
Robert M. Gray

2001 ◽  
Author(s):  
Shouda Jiang ◽  
Qi Wang ◽  
Sheng-He Sun

1996 ◽  
Author(s):  
Suryalakshmi Pemmaraju ◽  
Sunanda Mitra ◽  
L. Rodney Long ◽  
George R. Thoma ◽  
Yao-Yang Shieh ◽  
...  

Author(s):  
Ismahane Benyahia ◽  
Abdesselam Bassou ◽  
Chems El Houda Allaoui ◽  
Mohammed Beladgham

<span lang="EN-US">In this paper, an image compression method based on the Quincunx algorithm coupled with the modified SPIHT encoder (called SPIHT-Z) is presented. The SPIHT-Z encoder (coupled with quincunx transform) provides better compression results compared with two other algorithms: conventional wavelet and quincunx both coupled with the SPIHT encoder. The obtained results, using the algorithm that applies (Quincunx with SPIHT-Z) are evaluated by image quality evaluation parameters (PSNR, MSSIM, and VIF). The compression results on twenty test images showed that the proposed algorithm achieved better levels of the image evaluation parameters at low bit rates.</span>


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