Abstract:
The traditional Hard-Decision quantization is adopted in fixed offset, without considering the correlation between quantization coefficients, so that the quantitative per...Show MoreMetadata
Abstract:
The traditional Hard-Decision quantization is adopted in fixed offset, without considering the correlation between quantization coefficients, so that the quantitative performance is poor. To solve this problem, an improved Context Adaptive Hard-Decision quantization was proposed which introduces the coefficient correlation. Statistics out the quantitative offsets that corresponding non-zero coefficient segment when each coefficient is quantified and actual bit rate of each nonzero coefficient in quantization. Using Bayesian two value discrimination method calculates the best threshold value which can distinguish quantitative results and build up new threshold model, then use the new threshold model to adjust quantitative offsets dynamically. The experimental results show that the improved Context Adaptive Hard-Decision quantization model which takes the rate of nonzero coefficient segment into account is more efficient comparing with traditional hard-decision quantization.
Published in: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Date of Conference: 29-31 July 2017
Date Added to IEEE Xplore: 25 June 2018
ISBN Information: