Lattice Vector Quantization and the Lattice Sample-Adaptive Product Quantizers

격자 벡터 양자화와 격자 표본 적응 프로덕트 양자기

  • Kim, Dong-Sik (Department of Electronics Engineering, Hankuk University of Foreign Studies)
  • 김동식 (한국외국어대학교 전자공학과)
  • Received : 2011.07.02
  • Accepted : 2011.12.01
  • Published : 2012.03.25

Abstract

Optimal quantizers in conducting the entropy-constrained quantization for high bit rates have the lattice structure. The quantization process is simple due to the regular structure and various quantization algorithms are proposed depending on the lattice. In this paper, such a lattice vector quantization is implemented by using the sample-adaptive product quantizer (SAPQ). It is shown that several important lattices can be implemented by SAPQ and the lattice vector quantization can be performed by using a simple integer-transform function of scalar values within SAPQ. The performance of the proposed lattice SAPQ is compared to the entropy-constrained scalar quantizer and the entropy-constrained SAPQ (ECSAPQ) at a similar encoding complexity. Even though ECSAPQ shows a good performance at low bit-rates, lattice SAPQ shows better performance than the ECSAPQ case for a wide range of bit rates.

고 전송률에서 엔트로피 제한 양자화를 수행 시 최적의 양자기는 격자(lattice) 형태를 가지는데. 규칙적인 구조로 인하여 양자화 과정이 단순하며, 격자의 형태에 따라 여러 양자화 알고리듬이 제안되어있다. 본 논문에서는 이러한 격자 벡터 양자화를 표본 적응 프로덕트 양자기(sample-adaptive product quantizer: SAPQ)를 사용하여 구현하였다. 중요한 여러 격자들이 SAPQ의 단일화된 형태로 부호화된다는 사실을 보였으며, 스칼라 값의 정수 변환 함수를 사용하여 격자 벡터 양자화가 SAPQ를 통하여 간단히 구현될 수 있음을 보였다. 실험을 통하여 부호화 복잡도가 비슷한 ECSQ(entropy-constrained scalar quantizer), ECSAPQ(entropy-constrained SAPQ) 등과 성능을 비교하였는데, ECSAPQ는 저 전송률에서 좋은 성능을 보이는 반면 격자 SAPQ는 넓은 범위의 전송률에서 ECSAPQ보다 좋은 성능을 보임을 알 수 있었다.

Keywords

References

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