Generalized threshold replenishment: an adaptive vector quantization algorithm for the coding of nonstationary sources

摘要

In this paper, we describe a new adaptive-vector-quantization (AVQ) algorithm designed for the coding of non-stationary sources. This new algorithm, generalized threshold replenishment (GTR), differs from prior AVQ algorithms in that it features an explicit, online consideration of both rate and distortion. Because of its online nature, GTR is more amenable to real-time hardware and software implementation than are many prior AVQ algorithms that rely on traditional batch training methods. Additionally, as rate-distortion cost criteria are used in both the determination of nearest-neighbor codewords and the decision to update the codebook, GTR achieves rate-distortion performance superior to that of other AVQ algorithms, particularly for low-rate coding. Results are presented that illustrate low-rate performance surpassing that of other AVQ algorithms for the coding of both an image sequence and an artificial non-stationary random process. For the image sequence, it is shown that (1) most AVQ algorithms achieve distortion much lower than that of nonadaptive VQ for the same rate (about 1.5 b/pixel), and (2) GTR achieves performance substantially superior to that of the other AVQ algorithms for low-rate coding, being the only algorithm to achieve a rate below 1.0 b/pixel.