Abstract
In this paper, an entropy-constrained vector quantizer (ECVQ) scheme with finite memory called finite-state ECVQ (FS-ECVQ) is presented for saving the large memory requirements and improving the coding performance of an ordinary vector quantizer (VQ). This quantizer consists of a finite-state vector quantizer (FSVQ) and multiple ECVQs. The source sequence is first split into multiple clusters by the FSVQ. Then, to each cluster a dedicated ECVQ is applied. By the FSVQ, the FS-ECVQ effectively exploits the inter-frame dependencies, and moreover, there is no need to transmit the cluster indexes to the decoder side. By ECVQs, the ratedistortion (R/D) performance and the total memory requirements of the FS-ECVQ are able to be efficiently improved and decreased, respectively. A FS-ECVQ was implemented and evaluated based on USAC work draft 6 coding scheme. Results showed that compared to the original encoder, the proposed quantizer cut down the total memory requirements of 87% and improved the average coding gain to nine items of 3.87%, simultaneously.
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