Multiple description coding using transforms and data fusion
Multiple description coding using transforms and data fusion
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DOI：
10.1109/dcv.2002.1218761
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发表时间：
2005-04
期刊：
影响因子：
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通讯作者：
Sheng Tian;P. Rajan
中科院分区：
文献类型：
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作者：
Sheng Tian;P. Rajan
提出了一种新的多重描述编码技术，其中使用不同的变换生成多个描述，并采用线性融合来重建中心描述。这种新方法显示了多重描述矢量量化的一些优点，同时具有多重描述标量量化的复杂性。当将此方法应用于无内存高斯信号的固定级别标量量化时，如果侧面描述设计为以固定速率实现最小的失真，则中心描述的失真约为侧面描述的变形的一半。当应用于图像编码时，新技术会产生结果，在说明扭曲和实现的复杂性方面，这比当前的最新MD图像编码器要好。
A new multiple description coding technique is proposed, in which multiple descriptions are generated using different transforms and a linear fusion is employed to reconstruct the central description. This new method exhibits some of the advantages of multiple description vector quantization while having the complexity of multiple description scalar quantization. When this method is applied to fixed level scalar quantization of a memoryless Gaussian signal, the distortion of the central description is about half the distortion of side descriptions if the side descriptions have been designed to have the least distortion achievable at a fixed rate. When applied to image coding, the new technique yields result, which is better than current state-of-the-art MD image coders in terms of description distortions and complexity of implementation.