Densely connected autoencoders for image compression

Song Zebang, Kamata Sei-Ichiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (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.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Print)9781450360920
Publication statusPublished - 2019
Event2nd International Conference on Image and Graphics Processing, ICIGP 2019 - Singapore, Singapore
Duration: 2019 Feb 232019 Feb 25

Publication series

NameACM International Conference Proceeding Series
VolumePart F147765


Conference2nd International Conference on Image and Graphics Processing, ICIGP 2019


  • Convolutional neural networks (CNNs)
  • Densely AutoEncoders
  • Lossy image compression
  • U-net like structure

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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