A comparative analysis of the state-of-the-art lossless image compression techniques

Rahman, Asif; Hamada, Mohamed; Rahman, Asfaqur

doi:10.1051/shsconf/202213903001

Cited by 3 publications

(3 citation statements)

References 34 publications

(31 reference statements)

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“…Lossless image compression seeks to indicate an image signal with the least number of bits while maintaining all its information, which speeds up transmission and reduces storage requirements (6) . Although this method reduces the file sizes, compared to lossy compression, the reduction is comparatively smaller.…”

Section: Lossless Compressionmentioning

confidence: 99%

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A Novel Image Compression Algorithm Based on Autoencoder for Biometric Template Protection Scheme

Vasuki¹,

Prasanna²

2023

IJST

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Objectives: To evaluate the proposed deep learning-based method for biometric template protection schemes with other existing image compression methods. Methods: In this study, four image compression methods such as JPEG_LS, Vector quantization (VQ), Run-length encoding (RLE), and the Autoencoder methods are implemented. The experimental results are compared with the different performance parameters that are applied over the CASIA I iris dataset possessing 756 images. Findings: The proposed autoencoder method algorithm offered enhanced results in terms of PSNR, SSIM, MSE, and CR when compared to other transform methods. The deep neural network-based autoencoder algorithm achieved the highest compression ratio of 89.05 percent, while the conventional algorithms achieved the highest image quality rate of 94.05 percent. Novelty: A novel autoencoder-based image compression model has been proposed in this study. The proposed Autoencoder (AE) method incorporates five stages like Initialization, Learning compression representations, Generation of chaotic sequence, Image encryption and decryption, and Image reconstruction. The generation of the chaotic sequence using the stochastic logistic map, as well as the learning compression representations contributes towards the novelty of this study.

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Section: Lossless Compressionmentioning

confidence: 99%

“…Figures 5,6, 7 and 8 show the assessments of CR, Image Quality, PSNR, and BPP of the existing and proposed methods. The results indicated that the proposed method showed the highest compression ratio, better image quality, and better BPP when compared to the three existing methods.…”

mentioning

confidence: 99%

A Novel Image Compression Algorithm Based on Autoencoder for Biometric Template Protection Scheme

Vasuki¹,

Prasanna²

2023

IJST

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“…Finally, Rahman et al (2022) presented the results of the evaluation of various lossless image compression techniques. The methods were compared based on four datasets: EPFL Light-field, UVG-TUT, Kodak Lossless True Color Image Suite, and LCLi1k.…”

Section: Tellez Et Al (2020) Used a Neural Image Compression (Nic)mentioning

confidence: 99%

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

2023

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Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.

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Enhancing lossless compression algorithms using combining LZW and arithmetic coding

Kadhim,

Mosleh,

Abed

2024

AIP Conference Proceedings

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A comparative analysis of the state-of-the-art lossless image compression techniques

Cited by 3 publications

References 34 publications

A Novel Image Compression Algorithm Based on Autoencoder for Biometric Template Protection Scheme

A Novel Image Compression Algorithm Based on Autoencoder for Biometric Template Protection Scheme

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Enhancing lossless compression algorithms using combining LZW and arithmetic coding

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