Predictive quad-tree expansion technique for image compression in wavelet transform domain

2004 ◽  
Vol 13 (4) ◽  
pp. 878
Author(s):  
X. Li ◽  
E. Salari
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Transform Domain ◽  
Tree Expansion ◽  
Quad Tree ◽  
Expansion Technique

scholarly journals Deep Image Compression in the Wavelet Transform Domain Based on High Frequency Sub-Band Prediction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 52484-52497 ◽  
Author(s):  
Chuxi Yang ◽  
Yan Zhao ◽  
Shigang Wang
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
High Frequency ◽  
Transform Domain ◽  
Deep Image

Multiresolution Analysis of Fractal Image Compression

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 215-229
Author(s):  
Gregory Caso ◽  
C.-C. Jay Kuo
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Domain Structure ◽  
Multiresolution Analysis ◽  
Transform Coding ◽  
Transform Domain ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Affine Mappings ◽  
Encoding Method

In this research, we perform a multiresolution analysis of the mappings used in fractal image compression. We derive the transform-domain structure of the mappings and demonstrate a close connection between fractal image compression and wavelet transform coding using the Haar basis. We show that under certain conditions, the mappings correspond to a hierarchy of affine mappings between the subbands of the transformed image. Our analysis provides new insights into the mechanism underlying fractal image compression, leads to a new non-iterative transform-domain decoding algorithm, and suggests a new transform-domain encoding method with extensions to wavelets other than the Haar transform.

scholarly journals A Non-Blind Robust and Impercept Watermarking Using Discrete Cosine Transform and Discrete Wavelet Transform

2021 ◽  
pp. 69-76
Author(s):  
Eko Hari Rachmawanti ◽  
Heru Agus Santoso
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Discrete Wavelet Transform ◽  
Discrete Cosine Transform ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Cosine Transform ◽  
Advantages And Disadvantages ◽  
Bit Error Ratio

Non-blind watermarking is a form of watermarking with a watermark image validation process that requires a host image. The use of the transform domain is more robust and imperceptible. The transform domain method is resistant to various forms of digital image attacks. In this study, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) were selected as watermark insertion algorithms. DCT is faster and more resistant to attacks, especially in image compression attacks, but has lower imperceptibility than DWT. DWT is also known to be resistant to noise attacks, filtering, blurring, cropping, and has high imperceptibility depending on the sub-band selection but is not resistant to image compression attacks. Based on each algorithm's advantages and disadvantages, there is an opportunity to combine it to analyze and compare the insertion results with DCT and DWT itself. To test the results of imperceptibility, we used the Peak Signal to Noise Ratio (PSNR), while to test the robustness, we used Cross-Correlation (CC) and Bit Error Ratio (BER). Without attacks, the PSNR on the proposed method can reach 71 dB. The CC value without attack can reach a perfect value of 1 and BER = 0. The highest attack test result is CC = 1 on the filtering attack. From the various tests we have conducted, it has been proven that the DCT-DWT is more imperceptive and robust than previous studies

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