Canonical Huffman Coding Based Image Compression using Wavelet

The explosive growth of digital imaging, especially in the fields of medicine, education, and e-commerce, has made data maintenance and transmission over networks a daunting task. Therefore, the development and use of image compression techniques have become vital for overcoming the problems of stor...

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Bibliographic Details
Published in:Wireless personal communications Vol. 117; no. 3; pp. 2193 - 2206
Main Author: Ranjan, Rajiv
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2021
Springer Nature B.V
Subjects:
Algorithms
Coders
Communications Engineering
Computer Communication Networks
Computing time
Data compression
Digital imaging
Discrete cosine transform
Discrete Wavelet Transform
Engineering
Explosive compacting
Huffman codes
Image coding
Image compression
Image transmission
Networks
Signal,Image and Speech Processing
Wavelet transforms
Canonical huffman coding
Image compression
Discrete wavelet transform
Thresholding
ISSN:0929-6212, 1572-834X
Online Access:Get full text
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Summary:The explosive growth of digital imaging, especially in the fields of medicine, education, and e-commerce, has made data maintenance and transmission over networks a daunting task. Therefore, the development and use of image compression techniques have become vital for overcoming the problems of storage and transmission of digital image data. Two methods that are extensively used for data compression are Discrete Cosine Transformation and Discrete Wavelet Transform (DWT). In our present study, we have shown the benefits of a DWT-based approach by utilizing the canonical Huffman coding as an entropy encoder. DWT decomposes the image into different sub-bands. These sub bands are known as approximate image and detail images. The approximate image is normalized in the range (0, 1) for obtaining the Canonical Huffman coding bit stream. In a similar way, details coefficients are also normalized in the range (0, 1) for obtaining the canonical Huffman coding bit stream of detail images. Hard thresholding is often used to discard insignificant coefficients of detail images. Our proposed method takes less computing time and has a smaller codebook size than that of conventional Huffman coding. Moreover, the results show an improvement over Wavelet Scalar Quantization often used for image compression of fingerprints. We have applied our method to various popular images and obtained promising PSNR, CR, and BPP that highlight the advantages of our approach and the efficiency of our algorithms.
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ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-020-07967-y