A biometrie iris image compression using LZW and hybrid LZW coding algorithm

Sangeetha, M.; Betty, Paul; Kumar, Gaurav

doi:10.1109/iciiecs.2017.8275906

Cited by 16 publications

(6 citation statements)

References 6 publications

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“…There are five lossless compression techniques used in this study which are Elias Gamma Coding (EGC) by Trotman and Lilly (2018), Huffman Coding (HC) by Khaitu and Panday, (2018) Arithmetic Coding (AC) by Sarkar et al (2017), Run-Length Encoding (RLE) by Gupta et al (2017) Lempel Ziv Welch (LZW) (Sangeetha et al, 2017).…”

Section: Lossless Compression Techniquesmentioning

confidence: 99%

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Kadim,

Guirguis,

Elsayed

2024

Journal of Computer Science

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Image compression is a crucial task in image processing and in the process of sending and receiving files. There is a need for effective techniques for image compression as the raw images require large amounts of disk space to defect during transportation and storage operations. The most important objective of image compression is to decrease the redundancy of the image which helps in increasing the storage capacity and then efficient transmission. This study introduces a system for lossless image compression that is built to work on fingerprint image compression. It uses lossless compression to take care of the first image during processing. However, there is a serious problem which is the low ratio of compression. In order to make the ratio higher, there are five lossless compression techniques used in this study which are Elias Gamma Coding (EGC), Huffman Coding (HC), Arithmetic Coding (AC), Run-Length Encoding (RLE) and Lempel Ziv Welch (LZW). With these techniques, there are three types of transforms are used; they are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Discrete Shearlet Transform (DST). The results conclude that discrete shearlet transform with the Lempel-Ziv Welch coding technique outperforms the other lossless compression techniques and its Compression Ratio (CR) is 3.678023.

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Section: Lossless Compression Techniquesmentioning

confidence: 99%

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Kadim,

Guirguis,

Elsayed

2024

Journal of Computer Science

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“…Many of these applications necessitate a portable device capable of transmitting an iris image or template over a limited bandwidth communication channel. To ensure accurate recognition results, a fullresolution image (e.g., VGA) is typically required to provide sufficient pixel density across the iris [5].…”

Section: Introductionmentioning

confidence: 99%

Storage Space Reduction of Biometric Iris Databases by Successive Images Differences and Quadtree Decomposition

Hashim,

Jassim,

Hashim

2023

MMEP

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In recent years, the rise of biometric applications, particularly those centered around iris-based systems, has been significant. High data volumes inherent in these applications and the potential vulnerability of network links necessitate data compression in certain instances. The advantage of lossless compression methods is twofold: they maintain recognition performance without degradation while necessitating fewer computations for differentiation compared to their lossy counterparts. This study proposes a novel approach for lossless/lossy compression of iris biometric sample data across various public iris databases. Initially, the differences between successive images within each class are calculated, leveraging the strong correlation of images within each class. Subsequently, these differences are compressed using quadtree decomposition. This methodology was tested on six renowned iris databases: CASIA V1, CASIA V3, MMU1, MMU2, and UBIRIS Iris, all of which contain 8-bit grayscale images. The results indicate that the proposed strategy offers superior compression performance across different iris databases in comparison to existing methods. Notably, the results suggest that this method can be effectively integrated into an iris biometric recognition system, providing efficient iris image compression, especially when applied in its lossless form.

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“…The compression efficiency of the image will affect the speed of transmission and upload of the compressed file, which is an important part of evaluating the coding technology. However, in current studies, most of them just use one of these methods or simply improve it [1][2][3][4][5]. It is still blank to discuss the advantages and disadvantages of various methods in different situations.…”

Section: Introductionmentioning

confidence: 99%

Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms

2023

ACE

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Image compression technology is very popular in the field of image analysis because the compressed image is convenient for storage and transmission. In this paper, the Huffman algorithm and Lempel-Ziv-Welch (LZW) algorithm are introduced. They are widely used in the field of image compression, and the compressed image results of the two algorithms are calculated and compared. Based on the four dimensions of Compression Ratio (CR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), the applicable conditions of the two algorithms in compressing small image files are analysed. The results illustrated that when the source image files are less than 300kb, the Compression Ratio (CR) of Huffman algorithm was better than that of LZW algorithm. However, for Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), which are used to represent the compressed images qualities, LZW algorithm gave more satisfactory results.

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A biometrie iris image compression using LZW and hybrid LZW coding algorithm

Cited by 16 publications

References 6 publications

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Storage Space Reduction of Biometric Iris Databases by Successive Images Differences and Quadtree Decomposition

Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms

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