Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

Alosta, Mahmud; Souri, Alireza

doi:10.54216/fpa.060103

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Figure 7b,d For this study, the predictive approach was used to determine the threshold values, which were TH = 0.10. For both color (256 × 256 × 3 and 512 × 512 × 3) and grayscale (256 × 256 and 512 × 512) images, the principal component values of 25,25,200 and 400 were taken, respectively, to reconstruct the image.…”

Section: Visual Performance Evaluation Of Proposed Pca-dwt-chc Methodsmentioning

confidence: 99%

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An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

Ranjan,

Kumar

2023

Entropy

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Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

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Section: Visual Performance Evaluation Of Proposed Pca-dwt-chc Methodsmentioning

confidence: 99%

“…M. Alosta et al [25] examined the arithmetic coding for data compression. They measured the compression ratio and the bit rate to determine the extent of the image compression.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

Ranjan,

Kumar

2023

Entropy

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show abstract

“…Experiment shows that this method is suitable for compressing large image files with spatial redundancy. In [13], a lossless compression technique that becomes an arithmetic number was used to compress DNA sequences and experiments were conducted on artificial datasets and compared with other algorithms to achieve an improved compression of DNA sequences.…”

Section: Introductionmentioning

confidence: 99%

An adaptive image compression algorithm based on joint clustering algorithm and deep learning

Liang,

Zhao,

Liu

et al. 2023

IET Image Processing

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In recent years, deep artificial neural networks have attracted much attention and have been applied in various fields because they surpass the parameter fitting effect of traditional methods under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity make image compression necessary in communication. Here, a compression algorithm that combines the K‐means clustering algorithm with the neural network algorithm is proposed. First, the pixel points of the image are clustered by K‐means algorithm in order to reduce the amount of data input to the neural network algorithm. Secondly, neural network is used to extract image features which realizes further compression. The experiment results show that the peak signal‐to‐noise ratio (PSNR) is 33.48 dB at most with compression ratio at 32:1. The ablation experiment shows that the run time speeds up 9.5% compared to the algorithm without K‐means clustering. Comprehensive comparison experiment shows that the average PSNR is 30.09 dB, which is larger than other baseline approaches. The proposed algorithm is an efficient solution for image compression.

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Detection and Prevention of DoS Attack in VANET Using Artificial Neural Network

Adil,

Midha,

Srivastav

et al. 2024

2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)

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Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

Cited by 4 publications

References 15 publications

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

An adaptive image compression algorithm based on joint clustering algorithm and deep learning

Detection and Prevention of DoS Attack in VANET Using Artificial Neural Network

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