Study and analysis of wavelet based image compression techniques

Yadav, Rajesh Kumar; Gangwar, Som Pal; Singh, Harsh Vikram

doi:10.4314/ijest.v4i1.1s

Cited by 9 publications

(2 citation statements)

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“…After the previous step, the major data are stored in the approximation image on the top-left, while the other three quadrants hold less crucial information, so zeroing out data in these quadrants becomes a better choice. Typically, people choose from global and level-dependent thresholding [10]. Global thresholding uses a single value for all decomposition levels, whereas level-dependent thresholding applies a different value for each decomposition level.…”

Section: Wavelet Transform Image Compressionmentioning

confidence: 99%

A comparison of application of Fourier Transform and Wavelet Transform on image compression

Kan

2024

ACE

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This paper explores the application of Fourier Transform and Wavelet Transform in image compression, comparing their performance in terms of various parameters. As data compression continues to evolve to minimize storage and transmission costs, image compression techniques play a crucial role. Among the major compression methods, Fourier Transform and Wavelet Transform stand out. In this study, we introduce both transforms, elucidate their respective image compression methodologies, and undertake a comparative analysis of their compression qualities using multiple metrics. The mathematical underpinning of both transforms is discussed, and several quality metrics will serve as standards for comparison. In comparing the two techniques, Wavelet Transform consistently demonstrates better image compression quality, albeit with higher computational complexity. This study underscores the potential for further algorithmic enhancements to improve Fourier Transform-based compression and encourages an understanding of the trade-offs between compression quality and processing efficiency in the context of image compression techniques.

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Section: Wavelet Transform Image Compressionmentioning

confidence: 99%

A comparison of application of Fourier Transform and Wavelet Transform on image compression

Kan

2024

ACE

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“…However, as reported in [18], a fundamental shift in image compression scheme came after the DWT became popular. This popularity as well as DWT inherit multi-resolution nature aids its widespread acceptance in signal processing and image compression.…”

Section: Background On Image and Image Compressionmentioning

confidence: 99%

Comparative Performance Evaluation of Three Image Compression Algorithms

Popoola

Adekanye

2017

JASPE

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The advent of computer and internet has brought about massive change to the ways images are being managed. This revolution has resulted in changes in image processing and management as well as the huge space requirement for images’ uploading, downloading, transferring and storing nowadays. In guiding against this huge space requirement, images need to be compressed before either storing or transmitting. Several algorithms or techniques on image compression had been developed in literature. In this study, three of these image compression algorithms were developed using MATLAB codes. The three algorithms developed are discrete cosine transform (DCT), discrete wavelet transform (DWT) and set partitioning in hierarchical tree (SPIHT). In order to ascertain which of them is most appropriate for image storing and transmission, comparative performance evaluations were conducted on the three developed algorithms using five performance indices. The results of the comparative performance evaluations show that the three algorithms are effective in image compression but with different efficiency rates. In addition, the comparative performance evaluations results show that DWT has the highest compression ratio and distortion level while the corresponding values for SPIHT is the lowest with those of DCT fall in-between. Also, the results of the study show that the lower the mean square error and the higher the peak signal-to-noise-ratio, the lower the distortion level in the compressed image.

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