  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.
