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Artifacts Reduction for Compression Image with Pyramid Residual Convolutional Neural Network

Published: 25 February 2020 Publication History

Abstract

A pyramid residual convolutional neural network (PRCNN) is proposed to restore image with compression artifacts in this paper, and the method is enable to process multiple resolutions by fixing patch size extracted from whole image. Convolutional neural network has reached great performance in image processing (e.g. denoise, deblur, super-resolution), however deeper network may cause vanishing or exploding gradient problems, and it is hard to apply in realistic scene for high complexity. Thus, the residual blocks (RB) are proposed to balance between performance and application, besides, this paper exploits pyramid convolutional neural network to learn coarse-fine feature. In order to handle various resolutions, the fixed patch based method is used to adapt realistic scene. The experiment shows that the proposed algorithm can reduce compression artifacts through objective and subjective assessment, and the training/testing data are collected with H.264 coding. The proposed method can improve PSNR and SSIM from 0.54dB to 1.41dB, 0.01 to 0.04 while compression artifacts are reduced in visual quality, respectively.

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  • (2023)Mobile Image Compression Using Singular Value Decomposition and Deep LearningHybrid Intelligent Systems10.1007/978-3-031-27409-1_54(595-606)Online publication date: 25-May-2023

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cover image ACM Other conferences
ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing
December 2019
270 pages
ISBN:9781450376822
DOI:10.1145/3376067
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Shanghai Jiao Tong University: Shanghai Jiao Tong University
  • Xidian University
  • TU: Tianjin University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 February 2020

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Author Tags

  1. Compression artifacts
  2. fixed patch
  3. pyramid
  4. residual blocks

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Cited By

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  • (2023)Mobile Image Compression Using Singular Value Decomposition and Deep LearningHybrid Intelligent Systems10.1007/978-3-031-27409-1_54(595-606)Online publication date: 25-May-2023

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