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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Aggarwal, A. Sang-Uk Ryu Rose, K. |
| Copyright Year | 2003 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA (Aggarwal, A.; Sang-Uk Ryu; Rose, K.) |
| Abstract | The standard scalable coding of stereophonic audio suffers from significant performance loss because of (1) poor prediction gain at the enhancement-layer and (2) direct requantization of the reconstruction error, which is suboptimal for the noise-mask ratio (NMR) criterion. To mitigate such performance loss, this paper proposes an integrated approach which employs two complementary techniques, namely, the estimation theoretic (ET) predictor and the conditional enhancement-layer quantizer (CELQ). The ET predictor has been shown to combine information from various sources for efficient enhancement-layer prediction, while CELQ efficiently handles scalable quantization to minimize NMR. We demonstrate that the proposed combined approach can achieve major performance gains in terms of bit rate reduction and reconstruction quality enhancement. For example, the proposed 2/spl times/16 kbit/s two layer coder achieves considerably improved reconstruction quality compared to that of the conventional 4/spl times/16 kbit/s four layer coder, despite expending only 50% of the standard scalable coder bit rate. |
| Sponsorship | IEEE Signal Process, Soc |
| File Size | 295857 |
| File Format | |
| ISBN | 0780376633 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2003.1200007 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-04-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Quantization Bit rate Streaming media Nuclear magnetic resonance Performance loss Scalability Performance gain Signal to noise ratio Audio compression Redundancy |
| Content Type | Text |
| Resource Type | Article |
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