Dynamic Approximation of JPEG Hardware

Farhana Sharmin Snigdha, Deepashree Sengupta, Jiang Hu, Sachin S. Sapatnekar

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


JPEG compression based on the discrete cosine transform is a key building block in low-power multimedia applications. Approximate computation techniques are used to exploit the error tolerance of JPEG. An image-dependent framework is proposed in this paper to design optimized approximate hardware with variable approximate bit-widths for a user-specified error budget. The proposed method can dynamically adjust the extent of approximation in the system depending on the pixel values of the input image, thus leveraging the inherent sparsity of certain images. This novel technique not only improves the power-delay product by 3.4× over the base case, i.e., where the JPEG hardware is accurate but also significantly outperforms the image-independent approximation case, which is solely based on the error tolerance of the JPEG algorithm.

Original languageEnglish (US)
Article number8299430
Pages (from-to)295-308
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number2
StatePublished - Feb 2019

Bibliographical note

Funding Information:
Manuscript received August 12, 2017; revised December 9, 2017; accepted January 26, 2018. Date of publication February 21, 2018; date of current version January 18, 2019. This work was supported by NSF under Award CCF-1162267, Award CCF-1525925, and Award CCF-1525749. This paper was recommended by Associate Editor Y. Wang. (Corresponding author: Farhana Sharmin Snigdha.) F. Sharmin Snigdha, D. Sengupta, and S. S. Sapatnekar are with the Department of Electrical and Computer Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455 USA (e-mail: sharm304@umn.edu).

Funding Information:
Dr. Sengupta was a recipient of the Doctoral Dissertation Fellowship from the University of Minnesota.

Publisher Copyright:
© 2018 IEEE.


  • Approximate computing
  • JPEG
  • image compression
  • image contrast level
  • image-dependent approximation
  • image-independent approximation
  • low-power design
  • nonlinear optimization


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