IEEE Trans Image Process. 2018 Jul;27(7):3222-3235. doi: 10.1109/TIP.2018.2799704.
Millions of user-generated images are uploaded to social media sites like Facebook daily, which translate to a large storage cost. However, there exists an asymmetry in upload and download data: only a fraction of the uploaded images are subsequently retrieved for viewing. In this paper, we propose a cloud storage system that reduces the storage cost of all uploaded JPEG photos, at the expense of a controlled increase in computation mainly during download of requested image subset. Specifically, the system first selectively re-encodes code blocks of uploaded JPEG images using coarser quantization parameters for smaller storage sizes. Then during download, the system exploits known signal priors-sparsity prior and graph-signal smoothness prior-for reverse mapping to recover original fine quantization bin indices, with either deterministic guarantee (lossless mode) or statistical guarantee (near-lossless mode). For fast reverse mapping, we use small dictionaries and sparse graphs that are tailored for specific clusters of similar blocks, which are classified via tree-structured vector quantizer. During image upload, cluster indices identifying the appropriate dictionaries and graphs for the re-quantized blocks are encoded as side information using a differential distributed source coding scheme to facilitate reverse mapping during image download. Experimental results show that our system can reap significant storage savings (up to 12.05%) at roughly the same image PSNR (within 0.18 dB).
每天都有数以百万计的用户生成的图像上传到 Facebook 等社交媒体网站，这意味着存储成本很高。然而，上传和下载的数据存在不对称性：只有一小部分上传的图像随后被检索用于查看。在本文中，我们提出了一种云存储系统，可以减少所有上传的 JPEG 照片的存储成本，代价是在请求的图像子集下载期间主要增加受控的计算量。具体来说，该系统首先使用更粗糙的量化参数对上传的 JPEG 图像的代码块进行有选择地重新编码，以减小存储大小。然后，在下载期间，系统利用已知的信号先验——稀疏性先验和图形信号平滑性先验——进行反向映射，以恢复原始的精细量化-bin 索引，具有确定的保证（无损模式）或统计保证（近无损模式）。为了快速反向映射，我们使用了针对特定相似块簇量身定制的小字典和稀疏图，这些字典和稀疏图通过树状矢量量化器进行分类。在图像上传期间，用于重新量化块的适当字典和图形的簇索引使用差分分布式信源编码方案作为侧信息进行编码，以在图像下载期间促进反向映射。实验结果表明，我们的系统可以在几乎相同的图像 PSNR（在 0.18dB 以内）下获得显著的存储节省（高达 12.05%）。
