English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Finger print readers and retinal eye scanners have been an integral aspect of research and space organisations, forensic tests and so on. But to use it on a daily basis, Image compression is a necessary aspect considering the huge population of our country. Wavelet transform provides a platform for this process. In this paper, a novel idea to implement wavelet transform based on Daubechies wavelets has been discussed. The paper also discusses the concepts of linear transform, quantization, dequantization and image denoising. The image is first transformed using discrete wavelet transform to obtain the wavelet coefficients. The process of image compression is achieved by quantizing and encoding the coefficients. Uniform scalar quantization is done on the basis function coefficients in order to reduce their precision by reducing the number of bits required to store the transformed coefficients. The resulting quantized coefficients are then encoded using Huffman entropy encoding. In order to ensure that the image is free from disturbances like noise signals, denoising technique has been implemented using the wavelet coefficients. A paper of such an application has been proposed to tackle the global phenomenon of terrorism through the use of biometric devices including finger print reader and retinal eye scanner.

Daubechies Wavelet Based Compression and Denoising Techniques for Biometric Images