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Efficient image coding through compressive sensing and chaos theory

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Abstract

This paper introduces an image coding algorithm for gray scale images based on Block Compressive Sensing (BCS). The Original signal is sparse in Discrete Wavelet Transform (DWT) domain but the proposed work utilizes the virtue of encrypted DWT basis to employ sparsity. To generate encrypted DWT basis piecewise chaotic system is used which will provide more security to the signal. The Measurement Matrix (MM) is generated using hadamard matrix which is controlled by tent chaotic system. Further, the pixel values are mapped so that the values can be normalized in the desired range. Moreover, the seed values of chaotic map which is used to obtain MM and chaotic DWT basis, is used as encryption and decryption keys. To check the effectiveness of the proposed algorithm, it is tested on several test images and simulation results and detailed analysis has been done. It has been found that when the compression ratio is 0.5, the samples are half of the original signal, even then the reconstructed image is perceptually good. Further, the proposed algorithm is also examined against the statistical attacks and the results are fruitful as compared to the existing methods.

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Data Availability

The dataset which are used to analyzed the current study are available in the USC-SIPI ‘Miscellaneous: volume 3’ repository, https://sipi.usc.edu/database/database.php?volume=misc.

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Acknowledgements

This work is supported by Banaras Hindu University under the seed grant IoE (no. R/Dev/D/IoE/Seed Grant/2020-21/6031).

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Authors and Affiliations

  1. Computer Science, Banaras Hindu University, Varanasi, 221005, U.P., India

    Saumya Patel & Ankita Vaish

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  1. Saumya Patel
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  2. Ankita Vaish
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Correspondence to Saumya Patel.

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Patel, S., Vaish, A. Efficient image coding through compressive sensing and chaos theory. Multimed Tools Appl 82, 33225–33243 (2023). https://doi.org/10.1007/s11042-023-14946-5

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  • DOI: https://doi.org/10.1007/s11042-023-14946-5

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