Deep Convolutional AutoEncoder-based Lossy Image Compression

Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto

研究成果: Conference contribution

9 引用 (Scopus)

抜粋

Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.

元の言語English
ホスト出版物のタイトル2018 Picture Coding Symposium, PCS 2018 - Proceedings
出版者Institute of Electrical and Electronics Engineers Inc.
ページ253-257
ページ数5
ISBN(印刷物)9781538641606
DOI
出版物ステータスPublished - 2018 9 5
イベント33rd Picture Coding Symposium, PCS 2018 - San Francisco, United States
継続期間: 2018 6 242018 6 27

出版物シリーズ

名前2018 Picture Coding Symposium, PCS 2018 - Proceedings

Other

Other33rd Picture Coding Symposium, PCS 2018
United States
San Francisco
期間18/6/2418/6/27

    フィンガープリント

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology

これを引用

Cheng, Z., Sun, H., Takeuchi, M., & Katto, J. (2018). Deep Convolutional AutoEncoder-based Lossy Image Compression. : 2018 Picture Coding Symposium, PCS 2018 - Proceedings (pp. 253-257). [8456308] (2018 Picture Coding Symposium, PCS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PCS.2018.8456308