Scalable Learned Image Compression with A Recurrent Neural Networks-Based Hyperprior

Rige Su, Zhengxue Cheng, Heming Sun, Jiro Katto

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

Abstract

Recently learned image compression has achieved many great progresses, such as representative hyperprior and its variants based on convolutional neural networks (CNNs). However, CNNs are not fit for scalable coding and multiple models need to be trained separately to achieve variable rates. In this paper, we incorporate differentiable quantization and accurate entropy models into recurrent neural networks (RNNs) architectures to achieve a scalable learned image compression. First, we present an RNN architecture with quantization and entropy coding. To realize the scalable coding, we allocate the bits to multiple layers, by adjusting the layer-wise lambda values in Lagrangian multiplier-based rate-distortion optimization function. Second, we add an RNN-based hyperprior to improve the accuracy of entropy models for multiple-layer residual representations. Experimental results demonstrate that our performance can be comparable with recent CNN-based hyperprior methods on Kodak dataset. Besides, our method is a scalable and flexible coding approach, to achieve multiple rates using one single model, which is very appealing.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages3369-3373
Number of pages5
ISBN (Electronic)9781728163956
DOIs
Publication statusPublished - 2020 Oct
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: 2020 Sep 252020 Sep 28

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period20/9/2520/9/28

Keywords

  • RNN-based hyperprior
  • RNN-based image compression
  • entropy coding
  • quantization

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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