Improving Storage Efficiency for Raw Image Photo Repository by Exploiting Similarity

2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2016-12-01 DOI:10.1109/PDCAT.2016.045

Binqi Zhang, Chen Wang, B. Zhou, Albert Y. Zomaya

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Abstract

Exploiting temporal and spatial locality is a way to improve the performance of data compression and deduplication in a storage system. Through our evaluation, we find that content level similarity measures such as similar tags of photos have a certain correlation to data compressibility. Raw images with similar tags can be compressed together to get better storage space savings. Furthermore, storing similar raw images together enables rapid data sorting, searching, and retrieval if the images are stored in a distributed and large-scale environment with reduced fragmentation. In this paper, we present the correlation results between content similarity and data compressibility using a dataset built from Flickr. The system design we proposed has been based on the evaluation and it optimizes storage efficiency for Top-N relevant images with the same tag. On one hand, the storage space is saved. On the other hand, the design may accelerate the query performance for Top-N relevance search.

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利用相似性提高原始图像图片库的存储效率

利用时间和空间局部性是提高存储系统数据压缩和重复数据删除性能的一种方法。通过我们的评估，我们发现照片的相似标签等内容级相似度量与数据可压缩性有一定的相关性。具有相似标签的原始图像可以压缩在一起以更好地节省存储空间。此外，将相似的原始图像存储在一起可以实现快速的数据排序、搜索和检索，如果图像存储在分布式和大规模的环境中，并且碎片减少。在本文中，我们使用Flickr构建的数据集给出了内容相似度和数据可压缩性之间的相关结果。我们提出的系统设计是基于评估的，它优化了具有相同标签的Top-N相关图像的存储效率。一方面，节省了存储空间。另一方面，该设计可以加快Top-N相关性搜索的查询性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

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