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dc.contributor.authorBhatt, Upendra Mohan-
dc.date.accessioned2025-01-20T09:13:38Z-
dc.date.available2025-01-20T09:13:38Z-
dc.date.issued2023-05-
dc.identifier.urihttps://ieeexplore.ieee.org/abstract/document/10125692-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16828-
dc.description.abstractThe use of digital photographs has increased along with the development of digital technologies. Due to the vast amounts of information it contains, digital photographs need a lot of storage space, as well as bigger transmission bandwidths and longer transmission times. Therefore, on compressing the images all the redundant bits of information present in the image under test are removed while keeping only the essential information needed to reconstruct the image later on. In this study, DWT-SPIHT technique is introduced, which may be used to compress and reconstruct images at various degrees of wavelet decomposition across wavelet families that were initially a subdivision of the MATLAB wavelet family. Simulations have been conducted on Cameraman Image during this work of different resolution at different levels of decomposition and for different types of thresholding techniques to prove that this algorithm works well and provide us with the good reconstruction quality of the image. The simulation results demonstrate that, when compared to the DWT-EZW algorithm, the proposed DWT-SPIHT algorithm performs significantly better in terms of evaluation parameters like peak signal to noise ratio (PSNR), mean square error (MSE), and visual perception at higher compression ratios (CR) and low bit per pixel values (BPP).en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectEEEen_US
dc.subjectDiscrete Wavelet Transformen_US
dc.subjectEmbedded Zero Tree Waveleten_US
dc.subjectPeak Signal to Noise ratioen_US
dc.subjectCompression Ratioen_US
dc.subjectThresholdingen_US
dc.titleComparative Study and Analysis of DWT-SPIHT with DWT-EZW Method for Image Compressionen_US
dc.typeArticleen_US
Appears in Collections:Department of Electrical and Electronics Engineering

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