A novel image compression method using wavelet coefficients and Huffman coding

Title

A novel image compression method using wavelet coefficients and Huffman coding

Creator

Thomas S.; Krishna A.; Govind S.; Sahu A.K.

Description

Compressing medical images to reduce their size while maintaining their clinical and diagnostic information is crucial. Because medical images can be large and demand a lot of storage and transmission capacity, effective compression methods aid medical institutions in better storing and transmitting medical images, reducing costs, speeding up data transfer, and simplifying managing image databases. However, it is essential to note that image compression in medical imaging can also introduce drawbacks, such as loss of information and poor output image quality. Therefore, a suitable compression algorithm and parameter must be chosen to balance file size and visual fidelity. This paper suggests an effective image compression method employing the Discrete Wavelet Transform (DWT), followed by a reduction operation and Huffman coding to produce a mere lossless encoding to transmit the images over a channel. The extracted DWT coefficients are mapped to the nearest integral value. All four sub-bands of DWT are joined, and then a window of 3 3 is selected for reduction operation by choosing the origin as the pivot element. The Huffman coding algorithm is used to compress the processed image. The pivot origin element is used in the reversible reduction while uncompressing the image. When sending compressed data across an unreliable route, the window size and pivot element selection keep the compressed data secure. Standard measures such as bits per pixel (BPP) and compression ratio (CR) are used to assess the suggested approach. The efficiency of the suggested course of action is supported by the research's findings, which use a peak signal-to-noise ratio (PSNR) of 54.66 dB. 2023 The Authors

Source

Journal of Engineering Research (Kuwait)

Date

2024-01-01

Publisher

Elsevier B.V.

Subject

Bits per pixel; Compression ratio; Integer wavelet transform; Medical imaging; Visual quality

Coverage

Thomas S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India, Department of Computer Science, Rajagiri College of Social Sciences (Autonomous), Kerala, Kalamassery, India; Krishna A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Govind S., Department of Computer Science, Rajagiri College of Social Sciences (Autonomous), Kerala, Kalamassery, India; Sahu A.K., Amrita School of Computing, Amrita Vishwa Vidyapeetham, Andhra Pradesh, Amaravati, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 23071877

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1016/j.jer.2023.08.015

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