Patch-Based Holographic Image Sensing
This work addresses incremental improvements in distributed image storage and retrieval for applications requiring robust data access.
The paper tackles the problem of progressive image recovery from distributed storage by proposing patch-based holographic sensing optimized for random retrieval order, achieving improved recovery quality measured by the ℓ₂ norm as more data packets are retrieved.
Holographic representations of data enable distributed storage with progressive refinement when the stored packets of data are made available in any arbitrary order. In this paper, we propose and test patch-based transform coding holographic sensing of image data. Our proposal is optimized for progressive recovery under random order of retrieval of the stored data. The coding of the image patches relies on the design of distributed projections ensuring best image recovery, in terms of the $\ell_2$ norm, at each retrieval stage. The performance depends only on the number of data packets that has been retrieved thus far. Several possible options to enhance the quality of the recovery while changing the size and number of data packets are discussed and tested. This leads us to examine several interesting bit-allocation and rate-distortion trade offs, highlighted for a set of natural images with ensemble estimated statistical properties.