Low discrepancy holographic image sampling

Bruckstein, Alfred M.; Holt, Robert J.; Netravali, Arun N.

doi:10.1002/ima.20051

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Another approach, given in [5], proposed jittered quantization to achieve better precision with multiple instances of compressed image data exploiting differently quantized transform coefficients. A subsampling method for generating holographic data streams that ensure a uniform spatial spread of the sampled locations using low discrepancy sampling patterns can be found in [6].…”

Section: Introductionmentioning

confidence: 99%

Holographic sensing

Bruckstein

Ezerman

Fahreza

et al. 2020

Applied and Computational Harmonic Analysis

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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 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.

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Section: Introductionmentioning

confidence: 99%

Holographic sensing

Bruckstein

Ezerman

Fahreza

et al. 2020

Applied and Computational Harmonic Analysis

View full text Add to dashboard Cite

show abstract

“…Another approach, given in [7], proposed jittered quantization to achieve better precision with multiple instances of compressed image data exploiting differently quantized transform coefficients. A subsampling method for generating holographic data streams that ensure a uniform spatial spread of the sampled locations using low discrepancy sampling patterns can be found in [5].…”

mentioning

confidence: 99%

Patch-Based Holographic Image Sensing

Bruckstein¹,

Ezerman²,

Fahreza³

et al. 2021

SIAM J. Imaging Sci.

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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 2 norm, at each retrieval stage. The performance depends only on the number of data packets that have 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.

show abstract

Low discrepancy holographic image sampling

Cited by 2 publications

References 19 publications

Holographic sensing

Holographic sensing

Patch-Based Holographic Image Sensing

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