Vector quantisation (VQ) shows a good performance for image coding with high-compression ratios. However, there are many difficulties for image coding with VQ, especially the edge degradation and high-computational complexity. To resolve these two problems, the authors propose a new coding method based on edge orientation patterns (EOPs) by classifying image blocks into nine classes according to their edge orientations. For colour image coding, 27 codebooks (nine for each colour component) are pre-designed based on a series training images. In the encoding stage, an input colour image is decomposed into Y, Cb, and Cr components, and each component image is divided into non-overlapping 4 × 4 blocks. For each block, eight edge orientation templates of size 4 × 4 are performed to determine its edge orientation. According to the edge orientation, each block is compressed by using the corresponding codebook. Essentially, the authors’ scheme is a kind of classified VC (CVQ). Simulation results show that, their EOP-based CVQ can largely improve the compression efficiency as well as speeding up the encoding process and it is sufficient to establish effectiveness of the authors’ algorithm as compared with the existing techniques.

References

1. 1)
  - 13. Lai, J.Z.C., Yen, J.Y.: ‘Inverse halftoning of color images using classified vector quantization’, J. Vis. Commun. Image Represent., 1998, 9, (3), pp. 223–233.
2. 2)
  - 16. Horng, M.H.: ‘Vector quantization using the firefly algorithm for image compression’, Expert Syst. Appl., 2012, 39, (1), pp. 1078–1091.
3. 3)
  - 23. Li, J.: ‘Photography image database’. Available at http://www.stat.psu.edu/ ∼jiali/index.download.html.
4. 4)
  - 2. Chowdhury, M.M.H., Khatun, A.: ‘Image compression using discrete wavelet transform’, IJCSI Int. J. Comput. Sci. Issues, 2012, 9, (4), pp. 327–330.
5. 5)
  - 18. Poursistani, P., Nezamabadi-pour, H., Moghadam, R.A., et al: ‘Image indexing and retrieval in JPEG compressed domain based on vector quantization’, Math. Comput. Model., 2013, 57, (5), pp. 1005–1017.
6. 6)
  - 17. Kaushik, P., Sharma, Y.: ‘Comparison of different image enhancement techniques based upon PSNR & MSE’, Int. J. Appl. Eng. Res., 2012, 7, (11), PP. pp. 2010–2014.
7. 7)
  - 11. Bhagavatula, R., Heath, R.W.: ‘Predictive vector quantization for multicell cooperation with delayed limited feedback’, IEEE Trans. Wirel. Commun., 2013, 12, (6), pp. 2588–2597.
8. 8)
  - 15. Lu, Z.M., Feng, Y.P.: ‘Image retrieval based on histograms of EOPs and VQ indices’, Electron. Lett., 2016, 52, (20), pp. 1683–1684.
9. 9)
  - 8. Jiang, S., Yin, R., Liu, P.: ‘Finite-state entropy-constrained vector quantiser for audio modified discrete cosine transform coefficients uniform quantisation’, IET Signal Process., 2015, 9, (1), pp. 30–36.
10. 10)
  - 6. Ding, X., Lu, Z.M., Yu, F.X.: ‘A robust blind image watermarking scheme based on classified vector quantization’, J. Inf. Hiding Multimedia Signal Process., 2015, 6, (1), pp. 74–81.
11. 11)
  - 5. Gray, R.: ‘Vector quantization’, IEEE ASSP Mag., 1984, 1, (2), pp. 4–29.
12. 12)
  - 3. Kim, S.C., Kim, E.S.: ‘Fast computation of hologram patterns of a 3D object using run-length encoding and novel look-up table methods’, Appl. Opt., 2009, 48, (6), pp. 1030–1041.
13. 13)
  - 10. Chuang, J.C., Hu, Y.C., Lo, C.C.: ‘Improved mean-removed vector quantization scheme for grayscale image coding’, Int. J. Signal Process. Image Process. Pattern Recogn., 2013, 6, (5), pp. 315–332.
14. 14)
  - 24. Zhou, X., Bai, Y., Wang, C.: ‘Image compression based on discrete cosine transform and multistage vector quantization’, Int. J. Multimedia Ubiquit. Eng., 2005, 10, (6), pp. 347–356.
15. 15)
  - 20. Su, S.Z., Chen, S.Y., Li, S.Z., et al: ‘Structured local binary Haar pattern for pixel-based graphics retrieval’, Electron. Lett., 2010, 46, (14), pp. 996–998.
16. 16)
  - 1. Wang, J., Wu, Z., Jeon, G.: ‘An efficient spatial deblocking of images with DCT compression’, Digit. Signal Process., 2015, 42, pp. 80–88.
17. 17)
  - 22. Suhasini, P.S., Krishna, K., Krishna, I.M.: ‘CBIR using color histogram processing’, J. Theor. Appl. Inf. Technol., 2009, 6, (1), pp. 116–122.
18. 18)
  - 21. Kang, G.Y., Guo, S.Z., Wang, D.C., et al: ‘Image retrieval based on structured local binary kirsch pattern’, IEICE Trans. Inf. Syst., 2013, 96, (5), pp. 1230–1232.
19. 19)
  - 14. Kim, B.H., Kim, T.Y., Lee, J.W.: ‘DCT-based high speed vector quantization using classified weighted tree-structured codebook’. IEEE Int. Conf. on Systems, Man, and Cybernetics,, 1996, vol. 2, pp. 935–940.
20. 20)
  - 12. Ramamurthi, B., Gersho, A.: ‘Classified vector quantization of images’, IEEE Trans. Commun., 1986, 34, (11), pp. 1105–1115.
21. 21)
  - 4. Huffman, D.A.: ‘A method for the construction of minimum-redundancy codes’, Proc. IRE, 1952, 40, (9), pp. 1098–1101.
22. 22)
  - 9. Ma, X., Pan, Z., Hu, S.: ‘Enhanced side match vector quantisation based on constructing complementary state codebook’, IET Image Process., 2015, 9, (4), pp. 290–299.
23. 23)
  - 7. Nasrabadi, N.M., Feng, Y.: ‘Image compression using address-vector quantization’, IEEE Trans. Commun., 1990, 38, (12), pp. 2166–2173.
24. 24)
  - 25. Pan, J.S., Lu, Z.M., Sun, S.H.: ‘Image coding using SMVQ with two-level block classifier’. Proc. Int. Symposium on. Multimedia Software Engineering’., 2000, pp. 276–279.
25. 25)
  - 19. Chen, H.H., Ding, J.J., Sheu, H.T.: ‘Image retrieval based on quadtree classified vector quantization’, Multimedia Tools Appl., 2014, 72, (2), pp. 1961–1984.

Image coding based on classified vector quantisation using edge orientation patterns

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