Entropy-Constrained Sample-Adaptive Product Quantizer Design for the High Bit-Rate Quantization

고 전송률 양자화를 위한 엔트로피 제한 표본 적응 프로덕트 양자기 설계

  • Kim, Dong-Sik (Department of Electronics Engineering, Hankuk University of Foreign Studies)
  • 김동식 (한국외국어대학교 전자공학과)
  • Received : 2011.05.09
  • Accepted : 2011.12.01
  • Published : 2012.01.25

Abstract

In this paper, an entropy constrained vector quantizer for high bit-rates is proposed. The sample-adaptive product quantizer (SAPQ), which is based on the product codebooks, is employed, and a design algorithm for the entropy constrained sample adaptive product quantizer (ECSAPQ) is proposed. The performance of the proposed ECSAPQ is better than the case of the entropy constrained vector quantizer by 0.5dB. It is also shown that the ECSAPQ distortion curve, which is based on the scalar quantizer, is lower than the high-rate theoretical curve of the entropy constrained scalar quantizer, where the theoretical curve have 1.53dB difference from Shannon's lower bound.

본 논문에서는 고 전송률에서도 구현이 가능한 엔트로피 제한(entropy constrained) 벡터양자기를 제안하였다. 양자화는 프로덕트 부호책에 기초한 프로덕트 표본 적응 양자기(sample-adaptive product quantizer)를 사용하여 엔트로피 제한 SAPQ(ECSAPQ) 설계 알고리듬을 제안하고 실험을 통하여 성능을 비교해 보았다. 제안한 ECSAPQ는 비슷한 복잡도의 엔트로피 제한 VQ보다 약 0.5dB 정도 성능이 좋음을 알 수 있었다. 또한 스칼라 양자기에 기초한 ECSAPQ는 Shannon의 최저 왜곡 곡선과 1.53dB의 차이를 가지는 ECSQ의 고 전송률에서의 이론 곡선보다 더 낮은 왜곡 곡선을 가짐을 확인할 수 있었다.

Keywords

References

  1. A. J. Viterbi and J. K. Omura, Principles of Digital Communication and Coding. McGraw Hill, 1979.
  2. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Boston: Kluwer Academic Publishers, 1992.
  3. P. A. Chou, T. Lookabaugh, and R. M. Gray, "Entropy-constrained vector quantization," IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 37, no. 1, pp. 31-42, Jan. 1989. https://doi.org/10.1109/29.17498
  4. F. Kossentin, M. J. T. Smith, and C. F. Barnes, "Entropy-constrained residual vector quantization," in Proc. IEEE ICASSP, 1993, pp. V-598 -V-601.
  5. A. Gersho, "Asymptotically optimal block quantization," IEEE Trans. Inform. Theory, vol. 25, pp. 373-380, July 1979. https://doi.org/10.1109/TIT.1979.1056067
  6. M. Antonini, P. Raffy, M. Barlaud, "Towards entropy constrained lattice vector quantization," in Proc IEEE ICIP, vol. 1, 1995, pp.121-124.
  7. D. S. Kim and N. B. Shroff, "Quantization based on a novel sample-adaptive product quantizer (SAPQ)," IEEE Trans. Inform. Theory, vol. 45, no. 7, pp. 2306-2320, Nov. 1999. https://doi.org/10.1109/18.796371
  8. D. S. Kim and N. B. Shroff, "Sample-adaptive product quantization: asymptotic analysis and examples," IEEE Trans. Signal Processing, vol. 48, no. 10, pp. 2937-2947, Oct. 2000. https://doi.org/10.1109/78.869051
  9. J. H. Conway and N. J. A. Sloane, "Voronoi regions of lattices, second moments of polytopes, and quantization," IEEE Trans. Inform. Theory, vol. 28, no. 2, pp. 211-226, Mar. 1982. https://doi.org/10.1109/TIT.1982.1056483
  10. 김동식, "부호책 제한을 가지는 표본 적응 프로덕트 양자기를 이용한 1차 마르코프 과정의 고전송률 양자화," 대한전자공학회, 제 49권 SP편 제 1 호, 19-30쪽, 2012년 1월.
  11. Z. Raza, F. Alajaji, and T. Linder, "Design of sample adaptive product quantizers for noisy channels," IEEE Trans. Commun., vol. 53, no. 4, pp. 576-580, April 2005. https://doi.org/10.1109/TCOMM.2005.844938
  12. D. S. Kim and Y. Park, "Sample-adaptive product quantizers with affine index assignments for noisy channels," IEICE Trans. Commun., vol. E92-B, no. 10, pp. 3084-3093, Oct. 2009. https://doi.org/10.1587/transcom.E92.B.3084