Densely connected autoencoders for image compression

Song Zebang, Kamata Sei-Ichiro

研究成果: Conference contribution

3 被引用数 (Scopus)


Image compression, which is a type of data compression applied to digital images, has been a fundamental research topic for many decades. Recent image techniques produce very large amounts of data, which may make it prohibitive to storage and communications of image data without the use of compression. However, the traditional compression methods, such as JPEG, may introduce the compression artefact problems. Recently, deep learning has achieved great success in many computer vision tasks and is gradually being used in image compression. To solve the compression atrefact problem, in this paper, we present a lossy image compression architecture, which utilizes the advantages of the existing deep learning methods to achieve a high coding efficiency. We design a densely connected autoencoder structure for lossy image compression. Firstly, we design a densely autoencoder structure to get richer feature information from image which can be helpful for compression. Secondly, we design a U-net like network to decrease the distortion caused by compression. Finally, an improved binarizer is adopted to quantize the output of encoder. In low bit rate image compression, experiments show that our method significantly outperforms JPEG and JPEG2000 and can produce a better visual result with sharp edges, rich textures, and fewer artifacts.

ホスト出版物のタイトルACM International Conference Proceeding Series
出版社Association for Computing Machinery
出版ステータスPublished - 2019
イベント2nd International Conference on Image and Graphics Processing, ICIGP 2019 - Singapore, Singapore
継続期間: 2019 2月 232019 2月 25


名前ACM International Conference Proceeding Series
Part F147765


Conference2nd International Conference on Image and Graphics Processing, ICIGP 2019

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信


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