一种改进的上下文自适应硬决策量化算法

Xi Wei, Zhelei Xia
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引用次数: 0

摘要

在固定偏移量下采用传统的硬决策量化,没有考虑量化系数之间的相关性,量化性能较差。为了解决这一问题,提出了一种改进的上下文自适应硬决策量化方法,引入了相关系数。统计出各系数量化时所对应的非零系数段的量化偏移量和量化时各非零系数的实际比特率。利用贝叶斯二值判别法计算出能够区分定量结果的最佳阈值,建立新的阈值模型,然后利用新的阈值模型动态调整定量偏移量。实验结果表明,考虑非零系数段率的改进的上下文自适应硬决策量化模型比传统的硬决策量化更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved context adaptive hard-decision quantization algorithm
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.
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