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A Heuristic Algorithm for Arbitrary-criterion Optimization of Quantization Scales

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Abstract

The paper considers the generation of effective quantization scales which meet any quality criteria under any constrains. The analysis of work of available methods allows a concept of a heuristic iterative algorithm for generating near-optimal quantization scales. The concept uses a uniform scale as an initial approach followed by an iterative target-criterion-optimizing recalculation of quantization interval boundaries with adhering to the constrains at each iteration. The formal description of the algorithm is presented. The software implementation of the algorithm is incorporated into the hierarchical image-compression method. The numerical experiments are carried out to test the efficiency of the algorithm and substantiate the convergence of the algorithm to the best solution.

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REFERENCES

  1. Gersho, A. and Gray, R.M., Vector Quantization and Signal Compression, Springer Science & Business Media, 2012.

    MATH  Google Scholar 

  2. Perez-Delgado, M.L., Color image quantization using the shuffled-frog leaping algorithm, Eng. Appl. Artif. Intell., 2019, vol. 79, pp. 142–158.

    Article  Google Scholar 

  3. Nowakova, J., Prilepok, M., and Snasel, V., Medical image retrieval using vector quantization and fuzzy S‑tree, J. Med. Syst., 2017, vol. 41, no. 2, pp. 1–16.

    Article  Google Scholar 

  4. Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., … and Kalenichenko, D., Quantization and training of neural networks for efficient integer-arithmetic-only inference, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2704–2713.

  5. Zhang, D., Yang, J., Ye, D., and Hua, G., Lq-nets: Learned quantization for highly accurate and compact deep neural networks, Proceedings of the European conference on computer vision (ECCV), 2018, pp. 365–382.

  6. Polino, A., Pascanu, R., and Alistarh, D., Model compression via distillation and quantization, arXiv preprint, 2018, arXiv:1802.05668.

  7. Dumas, T., Roumy, A., and Guillemot, C., Autoencoder based image compression: can the learning be quantization independent, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 1188–1192.

  8. Gashnikov, M.V., Optimal quantization and adaptive interpolation in compression of multidimensional signals, Opt. Mem. Neural Networks, 2020, vol. 29, no. 2, pp. 133–141.

    Article  Google Scholar 

  9. Sayood, K., Introduction to Data Compression, Morgan Kaufmann, 2012.

    MATH  Google Scholar 

  10. Maksimov, A. and Gashnikov, M., Parameterized four direction contour-invariant extrapolator for DPCM image compression, Proceedings of SPIE 10th International Conference on Digital Image Processing (ICDIP), 2018, 10806, 108064E.

  11. Sergeyev, V.V., Glumov, N.I., and Gashnikov, M.V., Compression method for real-time systems of remote sensing, Proceedings of 15th IEEE International Conference on Pattern Recognition (ICPR), 2000, vol. 3, pp. 232–235.

  12. Gashnikov, M.V., Decision-Tree-Based Interpolation for Multidimensional Signal Compression, Proceedings of 8th International Symposium on Digital Forensics and Security (ISDFS), 2020, pp. 1–5.

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Funding

The work was partly funded by RFBR according to the research project 19-29-09045 (in parts 1–5), and the RF Ministry of Science and Higher Education within the state project of FSRC “Crystallography and Photonics” RAS (in part “Introduction”).

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Authors and Affiliations

  1. Samara National Research University, 443086, Samara, Russia

    M. V. Gashnikov

  2. IPSI RAS—Branch of the FSRC “Crystallography and Photonics” RAS, 443001, Samara, Russia

    M. V. Gashnikov

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  1. M. V. Gashnikov
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Correspondence to M. V. Gashnikov.

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Gashnikov, M.V. A Heuristic Algorithm for Arbitrary-criterion Optimization of Quantization Scales. Opt. Mem. Neural Networks 30, 140–145 (2021). https://doi.org/10.3103/S1060992X21020089

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  • DOI: https://doi.org/10.3103/S1060992X21020089

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