Auto-Regressive Model Enhanced Multiple Description Coding
碩士 === 國立交通大學 === 網路工程研究所 === 100 === Multiple description video coding (MDC) [1] is one of popular solutions to reduce the detrimental effects caused by transmission over error-prone networks. In this thesis, an auto-regressive model enhanced MDC is proposed. In general MDC architecture, redundancy...
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| Format: | Others |
| Language: | en_US |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/74788150929379530898 |
| Summary: | 碩士 === 國立交通大學 === 網路工程研究所 === 100 === Multiple description video coding (MDC) [1] is one of popular solutions to reduce the detrimental effects caused by transmission over error-prone networks. In this thesis, an auto-regressive model enhanced MDC is proposed. In general MDC architecture, redundancy rate and error resilience performance are important criterion for assessment. Auto-regressive model adopted in our proposal aims at reducing the redundancy rate while keeping the error resilience performance in our proposal. The proposed MDC model comprises two symmetric descriptions. One description is composed of even frames in h.264 standard and odd residual frames; while the other is omposed of odd frames and even residual frames. Both even and odd residual frames use the prediction frames generated by auto-regressive model. The experiments show that it achieves better coding efficiency and error resilience than descriptions which residual frames are predicted from interpolated frames in packet loss networks.
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