Lossy Compression for Embedded Computer Vision Systems

Li Guo, Dajiang Zhou, Jinjia Zhou, Shinji Kimura, Satoshi Goto

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Computer vision applications are rapidly gaining popularity in embedded systems, which typically involve a difficult trade-off between vision performance and energy consumption under a constraint of real-time processing throughput. Recently, hardware (FPGA and ASIC-based) implementations have emerged that significantly improve the energy efficiency of vision computation. These implementations, however, often involve intensive memory traffic that retains a significant portion of energy consumption at the system level. To address this issue, we are the first researchers to present a lossy compression framework to exploit the trade-off between vision performance and memory traffic for input images. To meet various requirements for memory access patterns in the vision system, a line-to-block format conversion is designed for the framework. Differential pulse-code modulation-based gradient-oriented quantization is developed as the lossy compression algorithm. We also present its hardware design that supports up to 12-scale 1080p@60fps real-time processing. For histogram of oriented gradient-based deformable part models on VOC2007, the proposed framework achieves a 49.6%–60.5% memory traffic reduction at a detection rate degradation of 0.05%–0.34%. For AlexNet on ImageNet, memory traffic reduction achieves up to 60.8% with less than 0.61% classification rate degradation. Compared with the power consumption reduction from memory traffic, the overhead involved for the proposed input image compression is less than 5%.

Original languageEnglish
JournalIEEE Access
DOIs
Publication statusAccepted/In press - 2018 Jul 3

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Keywords

  • Computer vision
  • Computer vision
  • Feature extraction
  • feature extraction
  • Hardware
  • Image coding
  • lossy compression
  • Memory management
  • memory traffic reduction
  • Power demand
  • Random access memory

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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