Improving Raw Image Storage Efficiency by Exploiting Similarity
This research provides incremental insights for designing large-scale storage systems by enhancing compression and retrieval efficiency for raw images.
The paper tackled the problem of improving storage efficiency for raw images by exploiting content-based similarity, finding that higher similarity leads to better compression results, with statistical evidence supporting this relationship.
To improve the temporal and spatial storage efficiency, researchers have intensively studied various techniques, including compression and deduplication. Through our evaluation, we find that methods such as photo tags or local features help to identify the content-based similar- ity between raw images. The images can then be com- pressed more efficiently to get better storage space sav- ings. Furthermore, storing similar raw images together enables rapid data sorting, searching and retrieval if the images are stored in a distributed and large-scale envi- ronment by reducing fragmentation. In this paper, we evaluated the compressibility by designing experiments and observing the results. We found that on a statistical basis the higher similarity photos have, the better com- pression results are. This research helps provide a clue for future large-scale storage system design.