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Practical estimation of adaptive correlation noise model for Distributed Video Coding

机译:分布式视频编码自适应相关噪声模型的实用估计

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In contrast with the traditional video compression system, Distributed Video Coding (DVC) architecture dramatically shifts the complexity from the encoder to the decoder. This low-cost encoding concept can be exploited in the emerging applications, e.g. wireless sensor networks. In order to increase the compression efficiency, improvement of side information generation and refinements of Correlation Noise Model (CNM) are main streams to improve DVC. However, most of these schemes are theoretical and expensive for the decoder. In order to retain low-cost and efficient system, a side information refinement with a practical CNM estimation is proposed. While maintaining the video quality, our proposed mechanism totally improves the system compression efficiency about 18% for the bit-rate with a low complexity decoder.
机译:与传统的视频压缩系统相比,分布式视频编码(DVC)体系结构将复杂性从编码器转移到了解码器。这种低成本的编码概念可以在新兴应用中得到利用,例如。无线传感器网络。为了提高压缩效率,改善辅助信息生成和完善相关噪声模型(CNM)是改善DVC的主要方法。然而,这些方案中的大多数对于解码器来说都是理论上的并且昂贵。为了保持低成本和高效的系统,提出了具有实用的CNM估计的辅助信息细化。在保持视频质量的同时,针对低复杂度解码器的比特率,我们提出的机制完全提高了系统压缩效率约18%。

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