Deep Convolutional AutoEncoder-based Lossy Image Compression

Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto

研究成果: Conference contribution

116 被引用数 (Scopus)


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.

ホスト出版物のタイトル2018 Picture Coding Symposium, PCS 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
出版ステータスPublished - 2018 9月 5
イベント33rd Picture Coding Symposium, PCS 2018 - San Francisco, United States
継続期間: 2018 6月 242018 6月 27


名前2018 Picture Coding Symposium, PCS 2018 - Proceedings


Other33rd Picture Coding Symposium, PCS 2018
国/地域United States
CitySan Francisco

ASJC Scopus subject areas

  • 信号処理
  • メディア記述


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