Enhanced Video Compression Based on Effective Bit Depth Adaptation

Aaron Zhang, Mariana Afonso, David Bull

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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This paper presents a novel Convolutional Neural Network (CNN) based effective bit depth adaptation approach (EBDA-CNN) for video compression. It applies effective bit depth down-sampling before encoding and reconstructs the original bit depth using a deep CNN based up-sampling method at the decoder. The proposed approach has been integrated with the High Efficiency Video Coding reference software HM 16.20, and evaluated under the Joint Video Exploration Team Common Test Conditions using the Random Access configuration. The results show consistent coding gains on all tested sequences, with an average bitrate saving of 6.4%, based on Bjøntegaard Delta measurements using PSNR.
Original languageEnglish
Title of host publication2019 26th IEEE International Conference on Image Processing (ICIP 2019)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
Publication statusAccepted/In press - 30 Apr 2019

Publication series

ISSN (Print)1522-4880


  • Effective bit depth adaptation
  • video compression
  • machine learning based compression
  • HEVC


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