Analysis of image compression methods based on wavelet transforms for maritime applications

The purpose of the article is to develop recommendations for choosing image compression method based on wavelet transformation, depending on image type, quality and compression requirements. Among the wavelet image compression methods, Embedded Zerotree Wavelet coder (EZW) and Set Partition In Hierarchical Trees (SPIHT) are considered, and the Haar wavelet and wavelet transformation in the oriented basis with the first, third, fifth and seventh decomposition levels is used as the base wavelet transform. These compression methods were compared with each other and with the standard JPEG method on the following parameters: mean square error, maximum error, peak to noise ratio, number of bits per pixel, compression ratio, and image size. The proposed methods can be successfully applied in the transmission of seabed relief images obtained from satellites or sea buoys.

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Comparative Compression of Wavelet Haar Transformation with Discrete Wavelet Transform on Colored Image Compression

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING ◽

10.31289/jite.v3i2.3154 ◽

2020 ◽

Vol 3 (2) ◽

pp. 202-209

Author(s):

Christnatalis Christnatalis ◽

Bachtiar Bachtiar ◽

Rony Rony

Keyword(s):

Wavelet Transform ◽

Image Compression ◽

Discrete Wavelet Transform ◽

Wavelet Transforms ◽

Wavelet Transformation ◽

Haar Wavelet ◽

Transformation Method ◽

Discrete Wavelet ◽

Digital Image Compression ◽

The Right

In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used even though it is compressed. The data used are data taken directly, so the test results are obtained that digital image compression based on Wavelet Wavelet Transformation gets a compression ratio of 41%, while the discrete wavelet transform reaches 29.5%. Based on research problems regarding the efficiency of storage media, it can be concluded that the right algorithm to choose is the Haar Wavelet transformation algorithm. To improve compression results it is recommended to use wavelet transforms other than haar, such as daubechies, symlets, and so on.

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MAMMOGRAM COMPRESSION TECHNIQUES USING HAAR WAVELET AND QUADTREE DECOMPOSITION-BASED IMAGE ENHANCEMENT

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237217500387 ◽

2017 ◽

Vol 29 (05) ◽

pp. 1750038

Author(s):

Basma A. Mohamed ◽

Heba M. Afify

Keyword(s):

Image Compression ◽

Processing Time ◽

Signal To Noise Ratio ◽

Haar Wavelet ◽

Image Size ◽

Signal To Noise ◽

Medical Field ◽

Biomedical Image ◽

Mammogram Image ◽

Mammogram Images

Biomedical image compression plays an important role in the medical field. Mammograms are medical images used in the early detection of breast cancer. Mammogram image compression is a challenging task because these images contain information that occupies huge size for storage. The aim of image compression is to reduce the image size and the time taken for recovering the original image without any loss. In this paper, two different techniques of mammogram compression are introduced. The proposed algorithm includes two main steps. First, a preprocessing step is applied to enhance the image, and then a compression algorithm is applied to the enhanced image. The algorithm is tested using 322 mammogram images from the online MIAS database. Three parameters are used to evaluate the performance of the compression techniques; compression ratio (CR), Peak Signal to Noise Ratio (PSNR) and processing time. According to the results, Haar wavelet-based compression for enhanced images is better in terms of CR of 26.25% and PSNR of 47.27[Formula: see text]dB.

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