Finite-state entropy-constrained vector quantiser for audio modified discrete cosine transform coefficients uniform quantisation

Sumxin Jiang; Rendong Yin; Peilin Liu

Finite-state entropy-constrained vector quantiser for audio modified discrete cosine transform coefficients uniform quantisation

View Fulltext

Author(s): Sumxin Jiang ¹ ; Rendong Yin ¹ ; Peilin Liu ¹
- Affiliations: 1: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
Source: Volume 9, Issue 1, February 2015, p. 30 – 36
DOI: 10.1049/iet-spr.2014.0089 , Print ISSN 1751-9675, Online ISSN 1751-9683

Received 03/03/2014, Accepted 16/06/2014, Revised 17/04/2014, Published 27/01/2015

In this paper, an entropy-constrained vector quantiser (ECVQ) scheme with finite memory, called finite-state ECVQ (FS-ECVQ), is presented. This scheme consists of a finite-state vector quantiser (FSVQ) and multiple component ECVQs. By utilising the FSVQ, the inter-frame dependencies within source sequence can be effectively exploited and no side information needs to be transmitted. By employing the ECVQs, the total memory requirements of FS-ECVQ can be efficiently decreased while the coding performance is improved. An FS-ECVQ, designed for the modified discrete cosine transform coefficients coding, was implemented and evaluated based on the unified speech and audio coding (USAC) scheme. Results showed that the FS-ECVQ achieved reduction of the total memory requirements by 92.3%, compared with the encoder in USAC working draft 6 (WD6), and over 10%, compared with the encoder in USAC final version (FINAL), while maintaining coding performance similar to FINAL, which was about 4% better than that of WD6.

References

1. 1)
  - 8. Bouzid, M., Cheraitia, S., Hireche, M.: ‘Switched split vector quantizer applied for encoding the LPC parameters of the 2.4 kbits/s MELP speech coder’. Seventh Int. Multi-Conf. on Systems Signals and Devices (SSD), 2010, June 2010, pp. 1–5.
2. 2)
  - E. Agrell , Eriksson , A. Vardy , K. Zeger . Closest point search in lattices. IEEE Trans. Inf. Theory , 2201 - 2214
3. 3)
  - 23. Gray, R., Linder, T., Li, J.: ‘A lagrangian formulation of Zador's entropy-constrained quantization theorem’, IEEE Trans. Inf. Theory, 2002, 48, (3), pp. 695–707 (doi: 10.1109/18.986007).
4. 4)
  - 21. Fuchs, G., Subbaraman, V., Multrus, M.: ‘Efficient context adaptive entropy coding for real-time applications’. 2011 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 493–496.
5. 5)
  - 18. Vasilache, A.: ‘Conditional split lattice vector quantization for spectral encoding of audio signals’. 14th European Signal Processing Conf., 2006. European Signal Processing Conf. on EUSIPCO 2006, September 2006, pp. 4–8.
6. 6)
  - 4. So, S., Paliwal, K.K.: ‘Efficient product code vector quantisation using the switched split vector quantiser’, Digit. Signal Process., 2007, 17, (1), pp. 138–171 (doi: 10.1016/j.dsp.2005.08.005).
7. 7)
  - 13. Chatterjee, S., Sreenivas, T.: ‘Switched conditional pdf-based split VQ using Gaussian mixture model’, IEEE Signal Process. Lett., 2008, 15, pp. 91–94 (doi: 10.1109/LSP.2007.910284).
8. 8)
  - N.M. Nasrabadi , S.A. Rizvi . Next-state function for finite state vector quantization. IEEE Trans. Image Process. , 12 , 1592 - 1601
9. 9)
  - 19. Information technology – MPEG audio technologies Part 3: unified speech and audio coding, ISO/IEC JTC 1/SC 29N11510 Std, 2010.
10. 10)
  - 15. Andras Cziho, B.S., IL ETC: ‘An optimization of finite-state vector quantization for image compression’, Signal Process. Image Commun., 2000, 15, (6), pp. 545–558 (doi: 10.1016/S0923-5965(99)00012-0).
11. 11)
  - 9. Leis, J., Sridharan, S.: ‘Adaptive vector quantization for speech spectrum coding’, Digit. Signal Process., 1999, 9, (2), pp. 89–106 (doi: 10.1006/dspr.1999.0335).
12. 12)
  - 26. Yu, R., Lin, X., Rahardja, S., Ko, C.: ‘A statistics study of the mdct coefficient distribution for audio’. 2004 IEEE Int. Conf. on Multimedia and Expo, 2004. ICME ’04, June 2004, vol. 2, pp. 1483–1486.
13. 13)
  - 11. Nordin, F., Eriksson, T.: ‘On split quantization of LSF parameters’. Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 2004. (ICASSP ’04), May 2004, vol. 1, pp. I–157–60.
14. 14)
  - 5. Subramaniam, A., Rao, B.: ‘PDF optimized parametric vector quantization of speech line spectral frequencies’, IEEE Trans. Speech Audio Process., 2003, 11, (2), pp. 130–142 (doi: 10.1109/TSA.2003.809192).
15. 15)
  - 12. Lee, Y., Jung, W., Kim, M.Y.: ‘GMM-based KLT-domain switched-split vector quantization for LSF coding’, IEEE Signal Process. Lett., 2011, 18, (7), pp. 415–418 (doi: 10.1109/LSP.2011.2154331).
16. 16)
  - 20. Nasrabadi, N., Choo, C., Feng, Y.: ‘Dynamic finite-state vector quantization of digital images’, IEEE Trans. Commun., 1994, 42, (5), pp. 2145–2154 (doi: 10.1109/26.285150).
17. 17)
  - 14. Zhao, D., Samuelsson, J., Nilsson, M.: ‘On entropy-constrained vector quantization using Gaussian mixture models’, IEEE Trans Commun., 2008, 56, (12), pp. 2094–2104 (doi: 10.1109/TCOMM.2008.070357).
18. 18)
  - K.K. Paliwal , B.S. Atal . Efficient vector quantization of LPC parameters at 24 bits/frame. IEEE Trans. Speech Audio Process , 1 , 3 - 14
19. 19)
  - 17. Jiang, S., Yin, R., Liu, P.: ‘A finite-state entropy-constrained vector quantizer for audio MDCT coefficients coding’. 2012 Int. Conf. on Audio, Language and Image Processing (ICALIP), 2012, pp. 218–223.
20. 20)
  - 6. So, S., Paliwal, K.: ‘Multi-frame GMM-based block quantisation of line spectral frequencies for wideband speech coding’. Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 2005. (ICASSP ’05), 18–23, 2005, vol. 1, pp. 121–124.
21. 21)
  - 28. Neuendorf, M., Gournay, P., Multrus, M., et al: ‘Unified speech and audio coding scheme for high quality at low bitrates’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009, April 2009, pp. 1–4.
22. 22)
  - 25. Gyorgy, A., Linder, T., Chou, P., Betts, B.: ‘Do optimal entropy-constrained quantizers have a finite or infinite number of codewords?’, IEEE Trans. Inf. Theory, 2003, 49, (11), pp. 3031–3037 (doi: 10.1109/TIT.2003.819340).
23. 23)
  - S. Chatterjee , T.V. Sreenivas . Optimal switched split vector quantisation of LSF parameters. Signal Process. , 6 , 1528 - 1538
24. 24)
  - P.A. Chou , T. Lookabaugh , R.M. Gray . Entropy-constrained vector quantization. IEEE Trans. Acoust. Speech Signal Process. , 1 , 31 - 42
25. 25)
  - 27. Gray, R., Linder, T.: ‘Mismatch in high-rate entropy-constrained vector quantization’, IEEE Trans. Inf. Theory, 2003, 49, (5), pp. 1204–1217 (doi: 10.1109/TIT.2003.810637).
26. 26)
  - 16. Yahampath, P., Pawlak, M.: ‘On finite-state vector quantization for noisy channels’, IEEE Trans. Commun., 2004, 52, (12), pp. 2125–2133 (doi: 10.1109/TCOMM.2004.838736).
27. 27)
  - 2. Vasilache, A.: ‘Rate-distortion models for entropy constrained lattice quantization’. 2010 IEEE Int. Conf. on Acoustics Speech and Signal Processing (ICASSP), March 2010, pp. 4698–4701.
28. 28)
  - 3. Gersho, A., Gray, R.M.: ‘Vector quantization and signal compression’ (Wiley, New York, 1994).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Finite-state entropy-constrained vector quantiser for audio modified discrete cosine transform coefficients uniform quantisation

References

Related content