低熵图像序列无损压缩

大规模的云渲染技术带来了大量的三维图形渲染数据。为了减小集群渲染产生的图像序列数据的传输以及存储代价,针对渲染图像序列低熵的特点,基于字典编码技术提出了降低数据局部复杂性的无损数据压缩方案。该方案通过数据重排技术来大大提高数据的局部冗余度,从而提高数据无损压缩效率。为了进一步解决大规模图像序列的压缩耗时问题,提出了一种云计算平台上的分布式图像压缩处理方案,充分利用现有云计算中Map/Reduce计算模型实现了分布式编码方案。实验结果证明,对于渲染产生的大规模低熵图像序列,提出的方案能够有效提高编码率并减少编码时间。

Low Entropy Image Sequences Lossless Compression

A large-scale amount of 3D graphic rendering image data has been brought because of the cloud rendering system.In order to reduce I/O transmission and storage cost of cluster rendered image sequences,this paper presented a lossless compression scheme based on dictionary technology which can get more compression by decreasing local complexity of data.A data rearrangement technology is applied to increase the degree of local redundancy,which can get more dense compression.To further improve the compression performance of a large-scale number of image sequences,the paper proposed a distributed image compression scheme based on the cloud computing infrastructure.The method was realized according to the current Map/Reduce computing model.The experimental results show that the approach proposed in this paper can make the coding process more efficiency.