Gaussian Noise and Haar Wavelet Transform Image Compression on Transmission of Dermatological Images

2011 ◽  
pp. 357-364 ◽  
Author(s):  
Kamil Dimililer ◽  
Cemal Kavalcıoğlu
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Gaussian Noise ◽  
Haar Wavelet ◽  
Haar Wavelet Transform

scholarly journals An efficient JPEG image compression based on Haar wavelet transform, discrete cosine transform, and run length encoding techniques for advanced manufacturing processes

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1532-1544
Author(s):  
Sulaiman Khan ◽  
Shah Nazir ◽  
Anwar Hussain ◽  
Amjad Ali ◽  
Ayaz Ullah
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Discrete Cosine Transform ◽  
Storage Capacity ◽  
Haar Wavelet ◽  
Manufacturing Processes ◽  
Advanced Manufacturing ◽  
Cosine Transform ◽  
Haar Wavelet Transform ◽  
Run Length

Image compression plays a key role in the transmission of an image and storage capacity. Image compression aims to reduce the size of the image with no loss of significant information and no loss of quality in the image. To reduce the storage capacity of the image, the image compression is proposed in order to offer a compact illustration of the information included in the image. Image compression exists in the form of lossy or lossless. Even though image compression mechanism has a prominent role for compressing images, certain conflicts still exist in the available techniques. This paper presents an approach of Haar wavelet transform, discrete cosine transforms, and run length encoding techniques for advanced manufacturing processes with high image compression rates. These techniques work by converting an image (signal) into half of its length which is known as “detail levels”; then, the compression process is done. For simulation purposes of the proposed research, the images are segmented into 8 × 8 blocks and then inversed (decoded) operation is performed on the processed 8 × 8 block to reconstruct the original image. The same experiments were done on two other algorithms, that is, discrete cosine transform and run length encoding schemes. The proposed system is tested by comparing the results of all the three algorithms based on different images. The comparison among these techniques is drawn on the basis of peak signal to noise ratio and compression ratio. The results obtained from the experiments show that the Haar wavelet transform outperforms very well with an accuracy of 97.8% and speeds up the compression and decompression process of the image with no loss of information and quality of image. The proposed study can easily be implemented in industries for the compression of images. These compressed images are suggested for multiple purposes like image compression for metrology as measurement materials in advanced manufacturing processes, low storage and bandwidth requirements, and compressing multimedia data like audio and video formats.

scholarly journals Analisis Perbandingan Kompresi Haar Wavelet Transform dengan Embedded Zerotree Wavelet pada Citra

2015 ◽  
Vol 3 (2) ◽  
pp. 161 ◽  
Author(s):  
LEDYA NOVAMIZANTI ◽  
ADRIAN KURNIA
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Haar Wavelet ◽  
Multimedia Technology ◽  
Priority Level ◽  
Haar Wavelet Transform ◽  
Compression Time ◽  
The Difference ◽  
Image Format ◽  
Wavelet Algorithm

ABSTRAKKompresi data merupakan salah satu teknologi pemicu revolusi multimedia. Haar Wavelet mampu merepresentasikan ciri tekstur dan bentuk, sedangkan Embedded Zerotree Wavelet (EZW) mampu menyusun bit-bit menurut tingkat prioritas, sehingga mampu mencapai kompresi maksimal. Pada penelitian ini telah dilakukan perbandingan Haar Wavelet Transform dengan Embendded Zerotree Wavelet untuk kompresi citra. Pengujian menggunakan 4 citra grayscale berformat bitmap (.bmp) dengan resolusi 256x256 dan 512x512. Rasio Kompresi yang diperoleh dengan menggunakan algoritma Embedded Zerotree Wavelet dan Haar Wavelet, yaitu 99.54% dan 95.35% pada threshold 80. Laju bit antara Embedded Zerotree Wavelet lebih rendah dibandingkan Haar Wavelet, yaitu 0.06 bpp dan 0.13 bpp. Algoritma Haar Wavelet memberikan waktu kompresi lebih baik dibandingkan EZW dimana selisih antara keduanya sekitar 8 detik.Kata kunci: kompresi citra,  threshold, Haar Wavelet, Embedded  Zerotree WaveletABSTRACTData compression is one of the triggers of the revolution multimedia technology. Haar Wavelet able to represent the characteristics of texture and shape, while Embedded Zerotree Wavelet (EZW) is able to arrange the bits according to priority level, so as to achieve maximum compression. In this study, we had conducted comparison between Haar Wavelet Transform with Embedded Zerotree Wavelet algorithm for image compression. The tests using 4 image format grayscale bitmap (.bmp) with resolution of 256x256 pixels and 512x512 pixels. Compression ratio obtained using Embedded Zerotree Wavelet and Wavelet Haar algorithm, which are 99.54% and 95.35% respectively, at the threshold of 80. The bit rate on Embedded Zerotree Wavelet is lower than Haar wavelet, that is 0:06 bpp and 0:13 bpp respectively. Haar Wavelet algorithm gives a better compression time than the EZW, with the difference between the two is about 8 seconds.Keywords: image compression, threshold, Haar Wavelet, Embedded  Zerotree Wavelet.

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