Exploring Resolution Fields for Scalable Image Compression With Uncertainty Guidance

Dongyi ZHANG, Feng LI, Man LIU, Runmin CONG, Huihui BAI*, Meng WANG, Yao ZHAO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

4 Citations (Scopus)

Abstract

Recently, there are significant advancements in learning-based image compression methods surpassing traditional coding standards. Most of them prioritize achieving the best rate-distortion performance for a particular compression rate, which limits their flexibility and adaptability in various applications with complex and varying constraints. In this work, we explore the potential of resolution fields in scalable image compression and propose the reciprocal pyramid network (RPN) that fulfills the need for more adaptable and versatile compression. Specifically, RPN first builds a compression pyramid and generates the resolution fields at different levels in a top-down manner. The key design lies in the cross-resolution context mining module between adjacent levels, which performs feature enriching and distillation to mine meaningful contextualized information and remove unnecessary redundancy, producing informative resolution fields as residual priors. The scalability is achieved by progressive bitstream reusing and resolution field incorporation varying at different levels. Furthermore, between adjacent compression levels, we explicitly quantify the aleatoric uncertainty from the bottom decoded representations and develop an uncertainty-guided loss to update the upper-level compression parameters, forming a reverse pyramid process that enforces the network to focus on the textured pixels with high variance for more reliable and accurate reconstruction. Combining resolution field exploration and uncertainty guidance in a pyramid manner, RPN can effectively achieve spatial and quality scalable image compression. Experiments show the superiority of RPN against existing classical and deep learning-based scalable codecs. Code will be available at https://github.com/JGIroro/RPNSIC.

Original languageEnglish
Pages (from-to)2934-2948
Number of pages15
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume34
Issue number4
Early online date22 Aug 2023
DOIs
Publication statusPublished - Apr 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • cross-resolution context mining
  • reciprocal pyramid network
  • resolution field
  • Scalable image compression
  • uncertainty-guided loss

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