Image Compression Using Vector Quantization

Agrawal, Rohit; Mohan, Narendra

doi:10.1007/978-981-16-9756-2_34

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Image compression using vector quantization is made by Agrawal and Mohan (2022), color image compression is made using vector quantization and hybrid wavelet transform by Kekre et al (2016), image compression using vector quantization is made also by Madhuri et al (2014), image compression using VQ for lossy compression is made by Chatterjee et al (2019), lossy image compression based on prediction error and vector quantization is made by Ayoobkhan et al (2017) and robust medical image compression based on wavelet transform and vector quantization is made by Ammah and Owusu (2019).…”

Section: Introductionmentioning

confidence: 99%

Non-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images Compression

Elsayed¹,

Majeed²,

Sherbiny³

2023

Journal of Computer Science

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This study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images. This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them. Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

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