Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Multi-Description Image Compression Coding Algorithm Based on Depth Learning

  • Yong Zhang (School of Computer Science, Civil Aviation Flight University of China) ;
  • Guoteng Hui (School of Computer Science, Civil Aviation Flight University of China) ;
  • Lei Zhang (Information Center, Civil Aviation Flight University of China)
  • Received : 2022.07.05
  • Accepted : 2022.11.06
  • Published : 2023.04.30
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Abstract

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.

Keywords

Acknowledgement

This study was supported from Civil Aviation Flight University of China.

References

  1. P. Li and K. T. Lo, "Survey on JPEG compatible joint image compression and encryption algorithms," IET Signal Processing, vol. 14, no. 8, pp. 475-488, 2020. https://doi.org/10.1049/iet-spr.2019.0276
  2. H. Yu and L. Y. Hu, "Fractal image texture detail enhancement based on Newton iteration algorithm," Computer Simulation, vol. 38, no. 2, pp. 263-266, 2021.
  3. R. Gupta, D. Mehrotra, and R. K. Tyagi, "Computational complexity of fractal image compression algorithm," IET Image Processing, vol. 14, no. 17, pp. 4425-4434, 2020. https://doi.org/10.1049/iet-ipr.2019.0489
  4. H. Pang and A. Zhang, "Sparse coding algorithm for fractal image compression based on coefficient of variation," Application Research of Computers, vol. 38, no. 8, pp. 2485-2489, 2021.
  5. L. Wang and Z. Liu, "Fast fractal image compression coding based on square weighted centroid feature," Telecommunication Engineering, vol. 60, no. 8, pp. 871-875, 2020.
  6. T. Tian, H. Wang, L. Zuo, C. C. J. Kuo, and S. Kwong, "Just noticeable difference level prediction for perceptual image compression," IEEE Transactions on Broadcasting, vol. 66, no. 3, pp. 690-700, 2020. https://doi.org/10.1109/TBC.2020.2977542
  7. W. Zhang, S. Zou, and Y. Liu, "Iterative soft decoding of Reed-Solomon codes based on deep learning," IEEE Communications Letters, vol. 24, no. 9, pp. 1991-1994, 2020. https://doi.org/10.1109/LCOMM.2020.2992488
  8. M. W. Li, D. Y. Xu, J. Geng, and W. C. Hong, "A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm," Nonlinear Dynamics, vol. 107, no. 3, pp. 2447-2467, 2022. https://doi.org/10.1007/s11071-021-07139-y
  9. J. S. Wekesa, J. Meng, and Y. Luan, "Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction," Genomics, vol. 112, no. 5, pp. 2928-2936, 2020. https://doi.org/10.1016/j.ygeno.2020.05.005
  10. V. Monga, Y. Li, and Y. C. Eldar, "Algorithm unrolling: interpretable, efficient deep learning for signal and image processing," IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 18-44, 2021. https://doi.org/10.1109/MSP.2020.3016905
  11. E. Balevi and J. G. Andrews, "Autoencoder-based error correction coding for one-bit quantization," IEEE Transactions on Communications, vol. 68, no. 6, pp. 3440-3451, 2020. https://doi.org/10.1109/TCOMM.2020.2977280