群体图像编码

利用图像间的相关性，对一组(群)图像进行联合编码，达到去除图像间冗余的目的，提高群体图像的压缩存储效率。传统的图像编码方法，如JPEG、JPEG2000等，只是针对单幅图像内部的冗余特性，通过预测、变换等编码方法对一幅图像进行压缩。而在当今大数据时代海量存储的图像数据间还存在着很大的冗余，有着进一步压缩的空间。比如人们会习惯于在同一地点、对于同一物体拍摄多张照片，这些照片可能有光照、人物表情姿势、拍摄角度等的差异，但景物内容的相同使得图片之间存在非常大相似性。不仅是同一相册的照片具有冗余性，不同人在同一地点拍摄的照片同样具有相关性，这些图像间存在大量信息冗余。因此，可以对图像之间的信息相关性加以利用，来进一步提升压缩效率。 对群体图像进行高效编码的关键是如何有效地组织这些相似图像，在图像间进行高效的预测编码。经典的组织结构有中心型预测[3]和最小生成树预测[9]。中心型预测即选择一副图像为预测中心，其他所有图像都参考这幅图像进行预测，比较适合图像间冗余度较高的群体图像，同时便于群体图像的插入、删除管理；最小生成树预测首先根据图像间冗余度建立一个最小生成树，然后按照深度优先的访问顺序，采用视频帧间预测的压缩方式进行编码。和中心型预测相比，最小生成树预测可以有更多参考图像进行预测，但这种灵活的预测结构也给群体图像的管理带来不便。

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