Adaptive-Length Coding of Image Data for Low-Cost Approximate Storage

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Abstract

In the past few years, ever-increasing amounts of image data have been generated by users globally, and these images are routinely stored in cold storage systems in compressed formats. This article investigates the use of approximate storage that leverages the use of cheaper, lower reliability memories that can have higher error rates. Since traditional JPEG-based schemes based on variable-length coding are extremely sensitive to error, the direct use of approximate storage results in severe quality degradation. We propose an error-resilient adaptive-length coding (ALC) scheme that divides all symbols into two classes, based on their frequency of occurrence, where each class has a fixed-length codeword. This provides a balance between the reliability of fixed-length coding schemes, which have a high storage overhead, and the storage-efficiency of Huffman coding schemes, which show high levels of error on low-reliability storage platforms. Further, we use data partitioning to determine which bits are stored in approximate or reliable storage to lower the overall cost of storage. We show that ALC can be used with general non-volatile storage, and can substantially reduce the total cost compared to traditional JPEG-based storage.

Original languageEnglish (US)
Article number8865439
Pages (from-to)239-252
Number of pages14
JournalIEEE Transactions on Computers
Volume69
Issue number2
DOIs
StatePublished - Feb 1 2020

Bibliographical note

Funding Information:
This work was supported in part by US National Science Foundation Grant no. CCF-1438286. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US National Science Foundation.

Publisher Copyright:
© 1968-2012 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Adaptive-length coding
  • approximate storage
  • error-resilience

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