Approximate-DCT-derived measurement matrices with row-operation-based measurement compression and its VLSI architecture for Compressed Sensing

Jianbin Zhou, Dajiang Zhou, Takeshi Yoshimura, Satoshi Goto

Research output: Contribution to journalArticle


Compressed Sensing based CMOS image sensor (CSCIS) is a new generation of CMOS image sensor that significantly reduces the power consumption. For CS-CIS, the image quality and data volume of output are two important issues to concern. In this paper, we first proposed an algorithm to generate a series of deterministic and ternary matrices, which improves the image quality, reduces the data volume and are compatible with CS-CIS. Proposed matrices are derived from the approximate DCT and trimmed in 2D-zigzag order, thus preserving the energy compaction property as DCT does. Moreover, we proposed matrix row operations adaptive to the proposed matrix to further compress data (measurements) without any image quality loss. At last, a low-cost VLSI architecture of measurements compression with proposed matrix row operations is implemented. Experiment results show our proposed matrix significantly improve the coding efficiency by BD-PSNR increase of 4.2 dB, comparing with the random binary matrix used in the-state-of-art CS-CIS. The proposed matrix row operations for measurement compression further increases the coding efficiency by 0.24 dB BD-PSNR (4.8% BD-rate reduction). The VLSI architecture is only 4.3 K gates in area and 0.3 mW in power consumption.

Original languageEnglish
Pages (from-to)263-272
Number of pages10
JournalIEICE Transactions on Electronics
Issue number4
Publication statusPublished - 2018 Apr 1



  • Approximate DCT
  • CMOS image sensor
  • Compressed sensing
  • Intra prediction
  • Measurement coding
  • Measurement matrix
  • VLSI architecture

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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