Dual-Stream Multi-Path Recursive Residual Network for JPEG Image Compression Artifacts Reduction

Zhi Jin, Muhammad Zafar Iqbal, Wenbin Zou, Xia Li, Eckehard Steinbach

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

JPEG is the most widely used lossy image compression standard. When using JPEG with high compression ratios, visual artifacts cannot be avoided. These artifacts not only degrade the user experience but also negatively affect many low-level image processing tasks. Recently, convolutional neural network (CNN)-based compression artifact removal approaches have achieved significant success, however, at the cost of high computational complexity due to an enormous number of parameters. To address this issue, we propose a dual-stream recursive residual network (STRRN) which consists of structure and texture streams for separately reducing the specific artifacts related to high-frequency or low-frequency image components. The outputs of these streams are combined and fed into an aggregation network to further enhance the restored images. By using parameter sharing, the proposed network reduces the total number of training parameters significantly. Moreover, experiments conducted on five commonly used datasets confirm that the proposed STRRN can efficiently reduce the compression artifacts, while using up to 4.6 times less training parameters and 5 times less running time compared to the state-of-the-art approaches.

Original languageEnglish
Article number9043584
Pages (from-to)467-479
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume31
Issue number2
DOIs
StatePublished - Feb 2021

Keywords

  • Dual-stream
  • compression artifacts reduction
  • recursive residual network
  • structure-texture decomposition

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