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<p>In this research, a new hybrid compression method is introduced, which is based on Beta function and singular vector sparse reconstruction (SVSR). The proposed technique incorporates wavelet filters whose coefficients are obtained from the beta function and its derivatives. These filters are employed to decompose the original crop image, and SVSR is used to enhance the compression efficiency. The efficacy of the suggested compression method is examined by testing it on different grayscale and color crop images and compared the results to other existing approaches. The assessment is based on fidelity parameters, including compression ratio (CR), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The Beta wavelet filters demonstrate superior compression ratios of 1.5% and 16.4% compared to Db4 and Bior4.4 wavelet filters, respectively. Additionally, they exhibit 47.8% and 80.13% higher compression ratios than SVSR and SVD, respectively, while maintaining approximately similar reconstruction quality. Experimental results show that the suggested compression technique achieves better performance than existing methods in terms of both image quality and compression efficiency. Therefore, it is concluded that this approach can be effectively utilized for grayscale and color crop image compression.</p>

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Beta Function Based Compression Method for Crop Image Using Singular Vector Sparse Reconstruction

  • 期刊论文

  • 发表日期：

    2025年4月11日

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  • Deepak Mishra,
  • Anil Kumar,
  • Vijaypal Singh Rathore

摘要

In this research, a new hybrid compression method is introduced, which is based on Beta function and singular vector sparse reconstruction (SVSR). The proposed technique incorporates wavelet filters whose coefficients are obtained from the beta function and its derivatives. These filters are employed to decompose the original crop image, and SVSR is used to enhance the compression efficiency. The efficacy of the suggested compression method is examined by testing it on different grayscale and color crop images and compared the results to other existing approaches. The assessment is based on fidelity parameters, including compression ratio (CR), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The Beta wavelet filters demonstrate superior compression ratios of 1.5% and 16.4% compared to Db4 and Bior4.4 wavelet filters, respectively. Additionally, they exhibit 47.8% and 80.13% higher compression ratios than SVSR and SVD, respectively, while maintaining approximately similar reconstruction quality. Experimental results show that the suggested compression technique achieves better performance than existing methods in terms of both image quality and compression efficiency. Therefore, it is concluded that this approach can be effectively utilized for grayscale and color crop image compression.