Quantization Bin Matching for Cloud Storage of JPEG Images

Abstract

Social media sites like Facebook are obligated to store all photos uploaded by an ever growing user base---which translates to an increasingly expensive storage cost---but only a fraction of uploaded images are revisited thereafter. In this paper, we propose a cloud storage system that trades off computation of a small fraction of requested images with storage of all photos. The key idea is to re-encode uploaded JPEG photos with coarser quantization parameters (QP) for permanent storage, then exploit a signal sparsity prior during inverse mapping to recover fine quantization bin indices via a maximum a posteriori (MAP) formulation. Because by design the system guarantees recovery of an original compressed image (either with exactly the same input fine quantization bin indices or has visual quality indistinguishable by human eyes), from the user's viewpoint it is a normal cloud storage, while from the operator's viewpoint there is pure compression gain and hence lower storage cost. Experimental results show that our storage system can reap significant storage savings (up to 20\%) at roughly the same image PSNR (within 0.1dB).