Abstract
To achieve a better trade-off between the vector dimension and the memory requirements of a vector quantizer (VQ), an entropy-constrained VQ (ECVQ) scheme with finite memory, called finite-state ECVQ (FS-ECVQ), is presented in this paper. The scheme consists of a finite-state VQ (FSVQ) and multiple component ECVQs. By utilizing the FSVQ, the inter-frame dependencies within source sequence can be effectively exploited and no side information needs to be transmitted. By employing the ECVQs, the total memory requirements of the FS-ECVQ can be efficiently decreased while the coding performance is improved. An FS-ECVQ, designed for the modified discrete cosine transform (MDCT) coefficients coding, was implemented and evaluated based on the Unified Speech and Audio Coding (USAC) scheme. Results showed that the FS-ECVQ achieved a reduction of the total memory requirements by about 11.3%, compared with the encoder in USAC final version (FINAL), while maintaining a similar coding performance.
Highlights
It is well known that a memoryless vector quantizer (VQ) can achieve performance arbitrarily close to the ratedistortion (R/D) function of the source, if the codevector dimension is large enough [1]
Among the widely reported product code techniques, split vector quantizer (SVQ), which was first proposed by Paliwal and Atal [6] for linear predictive coding (LPC) parameters quantization, receives extensive attention
Case: (a) Case: (b) Case: (c) where i denotes that the sub-finite-state VQ (FSVQ) belongs to the i-th entropy-constrained VQ (ECVQ) and t0, t1, and t2 are three constants making each combination of the four indices corresponding to a different current state
Summary
It is well known that a memoryless vector quantizer (VQ) can achieve performance arbitrarily close to the ratedistortion (R/D) function of the source, if the codevector dimension is large enough [1]. To better exploit the probability density function (pdf ) of the source, Chatterjee and Sreenivas [14] developed a switched conditional pdf-based SVQ where the vector space is partitioned into non-overlapping Voronoi regions, and the source pdf of each switching Voronoi region is modeled by a multivariate Gaussian These methods efficiently recover the split loss, most of them focus on removing intra-frame redundancies and fail to exploit inter-frame redundancies. This FSVQ predicts the source pdf of the current vector index based on the information obtained from its previous adjacent ones, and the length function with the highest matching probability is chosen Through this method, the ‘mismatch’ between the designed pdf and the source pdf can be efficiently decreased.
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