Constant-Time Gaussian Filtering for Acceleration of Structure Similarity

Tomohiro Sasaki, Norishige Fukushima, Yoshihiro Maeda, Kenjiro Sugimoto, Sei Ichiro Kamata

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

In this paper, we propose an acceleration method of structural similarity (SSIM) and its multi-scaled version, called MS-SSIM. The calculation process of SSIM and MS-SSIM includes multiple Gaussian filters, and the cost of the filter is dominant for the entire process; thus, to accelerate SSIM/MS-SSIM, we replace Gaussian filtering using convolution with sliding DCT. Gaussian filter based on sliding DCT is faster than the usual convolution method. Besides, its computational complexity does not depend on the filter window length. Also, naive implementations of SSIM and MS-SSIM scan image many times for the pixel-wise operation; however, these operations can be incorporated into Gaussian filtering. Thus, we optimize the processing pipeline to achieve high cache-efficiency. As a result, the proposed SSIM computation was accelerated by 6.36 times and MS-SSIM by 8.11 times faster than the conventional approach.

本文言語English
ホスト出版物のタイトルProceedings of International Conference on Image Processing and Robotics, ICIPRoB 2020
編集者B. H. Sudantha
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728165417
DOI
出版ステータスPublished - 2020 3 6
イベント1st International Conference on Image Processing and Robotics, ICIPRoB 2020 - Negombo, Sri Lanka
継続期間: 2020 3 62020 3 8

出版物シリーズ

名前Proceedings of International Conference on Image Processing and Robotics, ICIPRoB 2020

Conference

Conference1st International Conference on Image Processing and Robotics, ICIPRoB 2020
国/地域Sri Lanka
CityNegombo
Period20/3/620/3/8

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理
  • 決定科学(その他)
  • 制御と最適化

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