Perceptual Quality Study on Deep Learning Based Image Compression

Zhengxue Cheng, Pinar Akyazi, Heming Sun, Jiro Katto, Touradj Ebrahimi

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Recently deep learning based image compression has made rapid advances with promising results based on objective quality metrics. However, a rigorous subjective quality evaluation on such compression schemes have rarely been reported. This paper aims at perceptual quality studies on learned compression. First, we build a general learned compression approach, and optimize the model. In total six compression algorithms are considered for this study. Then, we perform subjective quality tests in a controlled environment using high-resolution images. Results demonstrate learned compression optimized by MS-SSIM yields competitive results that approach the efficiency of state-of-the-art compression. The results obtained can provide a useful benchmark for future developments in learned image compression.

本文言語English
ホスト出版物のタイトル2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
出版社IEEE Computer Society
ページ719-723
ページ数5
ISBN(電子版)9781538662496
DOI
出版ステータスPublished - 2019 9
イベント26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
継続期間: 2019 9 222019 9 25

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2019-September
ISSN(印刷版)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
国/地域Taiwan, Province of China
CityTaipei
Period19/9/2219/9/25

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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