The search functionality is under construction.
The search functionality is under construction.

End-to-End Deep ROI Image Compression

Hiroaki AKUTSU, Takahiro NARUKO

  • Full Text Views

    1

  • Cite this

Summary :

In this paper, we present the effectiveness of image compression based on a convolutional auto encoder (CAE) with region of interest (ROI) for quality control. We propose a method that adapts image quality for prioritized parts and non-prioritized parts for CAE-based compression. The proposed method uses annotation information for the distortion weights of the MS-SSIM-based loss function. We show experimental results using a road damage image dataset that is used to check damaged parts and an image dataset with segmentation data (ADE20K). The experimental results reveals that the proposed weighted loss function with CAE-based compression from F. Mentzer et al. learns some characteristics and preferred bit allocations of the prioritized parts by end-to-end training. In the case of using road damage image dataset, our method reduces bpp by 31% compared to the original method while meeting quality requirements that an average weighted MS-SSIM for the road damaged parts be larger than 0.97 and an average weighted MS-SSIM for the other parts be larger than 0.95.

Publication
IEICE TRANSACTIONS on Information Vol.E103-D No.5 pp.1031-1038
Publication Date
2020/05/01
Publicized
2020/01/24
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDP7264
Type of Manuscript
PAPER
Category
Artificial Intelligence, Data Mining

Authors

Hiroaki AKUTSU
  Hitachi, Ltd.
Takahiro NARUKO
  Hitachi, Ltd.

Keyword