Image Compression Using Wavelets and Vector Quantization Techniques

Authors

  • Varsha Purohit A. Assistant Professor, Department of Mathematics & Statistics, Banasthali University, Jaipur, Rajasthan, India

DOI:

https://doi.org/10.54741/asejar.2.2.3

Keywords:

image compression, lossless, lossy, huffman coding, fractal coding

Abstract

Digital image processing refers to the handling of an image by means of a processor. The various elements of a digital image processing system include image acquisition, image storage, image processing and display. Digital image compression has been focus of a huge amount of research in recent years. A survey and study of various image compression used in various image processing application has been presented. Image compression plays a vital role in Image processing, it is also very important for efficient transmission and storage of images. When calculate the number of bits per image resulting from typical sampling rates and quantization methods with image compression is needed. Therefore, development of an efficient technique for image compression has become a challenging one. On the basis of analyzing the various image compression technique, this paper presents a survey of existing researcher has various kinds of existing method of Image compression.

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References

Vijayvargiya G., Silakari S., & Pandey R (2013). A survey: Various techniques of image compression. International Journal of Computer Science and Information Security, 11(10).

Vrindavanam J., Chandran S., & Mahanti G.K. (2012). A survey of image compression methods. International Conference and Workshop on Recent Trends in Technology, (TCET), pp. 12-17.

Neelam & Bansal A. (2014). Image compression a learning approach : Survey. International Journal of Computer Science Trends and Technology, 2(4), 60–66.

Suganya M., Ramachandran A., Venugopal D., & Sivanantha Raja A. (2014). Lossless compression and efficient reconstruction of colour medical images. International Journal of Innovative Research in Computer and Communication Engineering, 2(Special Issue 1), 1271–1278.

Jadhav T., Patil M., & Dandawate Y. (2015). Image compression using mean removed and multistage vector quantization in wavelet domain. International Journal of Modern Trends in Engineering and Research, 1299–1306.

Kumar T., & Kumar R. (2015). Medical image compression using hybrid techniques of DWT, DCT and huffman coding. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 3(2), 54-60.

Saravanan S. (2013). Medical image compression using curvelet transform. International Journal of Engineering Research and Technology, 2(12), 2196–2202.

Jenny C.T., & Muthulakshmi G. (2010). A modified embedded zero-tree wavelet method for medical image compression. ICTACT Journal on Image and Video Processing, 02, 87-91.

Kaur A., & Goyal M. (2014). ROI based image compression of medical images. International Journal of Computer Science Trends and Technology, 2(5), 162-166.

Ruchika, Singh M., & Singh A.R. (2012). Compression of medical images using wavelet transforms. International Journal of Soft Computing and Engineering, 2(2), 339 -343.

Md. Mahmudul Hassan, & Wang Xuefeng. (2022). The challenges and prospects of inland waterway transportation system of Bangladesh. International Journal of Engineering and Management Research, 12(1), 132-143.

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Published

2023-03-31

How to Cite

Varsha Purohit A. (2023). Image Compression Using Wavelets and Vector Quantization Techniques. Applied Science and Engineering Journal for Advanced Research, 2(2), 14–18. https://doi.org/10.54741/asejar.2.2.3

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Articles