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Efficient image compression based on side match vector quantization and digital inpainting

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Abstract

In this paper, we propose two efficient compression schemes for digital images using an adaptive selection mechanism for vector quantization (VQ), side match vector quantization (SMVQ), and image inpainting. On the sender side, after the original image is divided into blocks, the compression is implemented block by block. In both schemes, blocks in pre-specified locations are first compressed by VQ. For each remaining block, the optimal compression method (for the first scheme, including VQ or inpainting, and for the second scheme, including VQ, SMVQ, and inpainting) is determined by computing the mean square error (MSE) between the original block and its inpainted result and then comparing it with a predefined threshold. If MSE is greater than the threshold, image inpainting continues to be used to compress the current block. Otherwise, the compression mode of VQ or SMVQ is selected to substitute image inpainting to maintain higher visual quality. With the assistance of transmitted indicator flags, the receiver side can execute the image inpainting and decompression successfully. Experimental results demonstrate the effectiveness and superiority of two proposed schemes.

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References

  1. Taubman, D.S., Marcellin, M.W.: JPEG 2000: image compression fundamentals standards and practice. Springer Int. 11(2), 286 (2001)

    Google Scholar 

  2. Gersho, A., Gray, R.M.: Vector quantization and signal compression. Springer Int. 159(1), 407–485 (1992)

    MATH  Google Scholar 

  3. Ancis, M., Giusto, D.D.: Image data compression by adaptive vector quantization of classified wavelet coefficients. IEEE Pac. Rim Conf. Commun. Comput. Signal Process. 1(1), 330–333 (1997)

    Google Scholar 

  4. Nasrabadi, N.M., King, R.: Image coding using vector quantization: a review. IEEE Trans. Commun. 36(8), 957–971 (1988)

    Article  Google Scholar 

  5. Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980)

    Article  Google Scholar 

  6. Chang, C.C., Wu, W.C.: Fast planar-oriented ripple search algorithm for hyperspace VQ codebook. IEEE Trans. Image Process. 16(6), 1538–1547 (2007)

    Article  MathSciNet  Google Scholar 

  7. Tang, H., Zhang, J., Sun, J., Qiu, T., Park, Y.: Phonocardiogram signal compression using sound repetition and vector quantization. Comput. Biol. Med. 71, 24–34 (2016)

    Article  Google Scholar 

  8. Cohen, L.D.: A new approach of vector quantization for image data compression and texture detection. Int. Conf. Pattern Recognit. 2(2), 1250–1254 (1988)

    Google Scholar 

  9. Cockshott, W.P., Tao, Y., Gao, G., Daly, C.: Microscopic volumetric image data compression using vector quantization and 3D pyramid. IEICE Trans. Commun. 77(6), 770–780 (2008)

    Google Scholar 

  10. Qin, C., Ji, P., Wang, J.W., Chang, C.C.: Fragile image watermarking scheme based on VQ index sharing and self-embedding. Multimed. Tools Appl. 76(2), 2267–2287 (2017)

    Article  Google Scholar 

  11. Qin, C., Hu, Y.C.: Reversible data hiding in VQ index table with lossless coding and adaptive switching mechanism. Signal Process. 129, 48–55 (2016)

    Article  Google Scholar 

  12. Qin, C., Ji, P., Chang, C.C., Dong, J., Sun, X.M.: Non-uniform watermark sharing based on optimal iterative BTC for image tampering recovery. IEEE Multimed. (2018). https://doi.org/10.1109/MMUL.2018.112142509

    Google Scholar 

  13. Qin, C., Chen, X.Q., Ye, D.P., Wang, J.W., Sun, X.M.: A novel image hashing scheme with perceptual robustness using block truncation coding. Inf. Sci. 361–362, 84–99 (2016)

    Article  Google Scholar 

  14. Kim, T.: Side match and overlap match vector quantizers for images. IEEE Trans. Image Process. 1(2), 170–185 (1992)

    Article  MathSciNet  Google Scholar 

  15. Kambel, S.S., Deshpande, A.S.: Image compression based on side match vector quantization. Int. J. Eng. Sci. Comput. 6(5), 5777–5779 (2016)

    Google Scholar 

  16. Lin, S.D.: Side-match finite-state vector quantization with adaptive block classification for image compression. IEICE Trans. Inf. Syst. 83(8), 1671–1678 (2000)

    Google Scholar 

  17. Chang, C.C., Shiue, F.C., Chen, T.S.: Pattern-based side match vector quantization for image compression. Image Sci. J. 48(2), 63–76 (2000)

    Article  Google Scholar 

  18. Qin, C., Chang, C.C., Chiu, Y.P.: A novel joint data-hiding and compression scheme based on SMVQ and image inpainting. IEEE Trans. Image Process. 23(3), 969–978 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Qin, C., He, Z.H., Yao, H., Cao, F., Gao, L.P.: Visible watermark removal scheme based on reversible data hiding and image inpainting. Signal Process. Image Commun. 60, 160–172 (2018)

    Article  Google Scholar 

  20. Qin, C., Chang, C.C., Huang, Y.H., Liao, L.T.: An inpainting-assisted reversible steganographic scheme using a histogram shifting mechanism. IEEE Trans. Circuits Syst. Video Technol. 23(7), 1109–1118 (2013)

    Article  Google Scholar 

  21. Chan, T.F., Shen, J.: Mathematical models for local non-texture inpaintings. SIAM J. Appl. Math. 62(3), 1019–1043 (2001)

    MathSciNet  MATH  Google Scholar 

  22. Wen, Y.W., Chan, R.H., Yip, A.M.: A primal-dual method for total-variation-based wavelet domain inpainting. IEEE Trans. Image Process. 21(1), 106–114 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  23. Hsieh, C.H., Tsai, J.C.: Lossless compression of VQ index with search-order coding. IEEE Trans. Image Process. 5(11), 1579–1582 (1996)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61702332, 61672354). The authors would like to thank the anonymous reviewers for their valuable comments.

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Correspondence to Heng Yao.

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Zhou, Q., Yao, H., Cao, F. et al. Efficient image compression based on side match vector quantization and digital inpainting. J Real-Time Image Proc 16, 799–810 (2019). https://doi.org/10.1007/s11554-018-0800-1

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  • DOI: https://doi.org/10.1007/s11554-018-0800-1

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