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Content-adaptive image downscaling

Authors:

Pieter PeersAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 32, Issue 6

Article No.: 173, Pages 1 - 8

https://doi.org/10.1145/2508363.2508370

Published: 01 November 2013 Publication History

Abstract

This paper introduces a novel content-adaptive image downscaling method. The key idea is to optimize the shape and locations of the downsampling kernels to better align with local image features. Our content-adaptive kernels are formed as a bilateral combination of two Gaussian kernels defined over space and color, respectively. This yields a continuum ranging from smoothing to edge/detail preserving kernels driven by image content. We optimize these kernels to represent the input image well, by finding an output image from which the input can be well reconstructed. This is technically realized as an iterative maximum-likelihood optimization using a constrained variation of the Expectation-Maximization algorithm. In comparison to previous downscaling algorithms, our results remain crisper without suffering from ringing artifacts. Besides natural images, our algorithm is also effective for creating pixel art images from vector graphics inputs, due to its ability to keep linear features sharp and connected.

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Cited By

Wu PWang SShen LWang FTian ZHua X(2024)Multi-Prior Driven Resolution Rescaling Blocks for Intra Frame CodingIEEE Transactions on Multimedia10.1109/TMM.2024.345303326(11274-11289)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3453033
Sacht L(2024)Bottle cap art via clustering and optimal color assignmentsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03512-140:10(6929-6937)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00371-024-03512-1
Lei PXu SZhang S(2024)An art-oriented pixelation method for cartoon imagesThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02763-040:1(27-39)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00371-022-02763-0
Show More Cited By

Index Terms

Content-adaptive image downscaling
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition

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Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 32, Issue 6

November 2013

671 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2508363

Issue’s Table of Contents

Copyright © 2013 ACM.

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2013

Published in TOG Volume 32, Issue 6

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Cited By

Wu PWang SShen LWang FTian ZHua X(2024)Multi-Prior Driven Resolution Rescaling Blocks for Intra Frame CodingIEEE Transactions on Multimedia10.1109/TMM.2024.345303326(11274-11289)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3453033
Sacht L(2024)Bottle cap art via clustering and optimal color assignmentsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03512-140:10(6929-6937)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00371-024-03512-1
Lei PXu SZhang S(2024)An art-oriented pixelation method for cartoon imagesThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02763-040:1(27-39)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00371-022-02763-0
Song SZhong FWang TQin XTu C(2023)Guided Linear UpsamplingACM Transactions on Graphics10.1145/359245342:4(1-12)Online publication date: 26-Jul-2023
https://doi.org/10.1145/3592453
Igarashi YIgarashi T(2023)Pixel Art Adaptation for Handicraft FabricationComputer Graphics Forum10.1111/cgf.1469441:7(489-494)Online publication date: 20-Mar-2023
https://doi.org/10.1111/cgf.14694
Xing JHu WXia MWong T(2023)Scale-Arbitrary Invertible Image DownscalingIEEE Transactions on Image Processing10.1109/TIP.2023.329689132(4259-4274)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3296891
Huang CSun WChen Z(2023)Learned Scale-arbitrary Image Downscaling for Non-learnable UpscalingIEEE Signal Processing Letters10.1109/LSP.2023.3259820(1-5)Online publication date: 2023
https://doi.org/10.1109/LSP.2023.3259820
Qi CYang XCheng KChen YChen Q(2023)Real-time 6K Image Rescaling with Rate-distortion Optimization2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01354(14092-14101)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.01354
Ghosh SGarai A(2023)Image downscaling via co-occurrence learningJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.10376691:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.jvcir.2023.103766
Zhou YWu HLiu WXiong ZQin JHe S(2023)Single-View View Synthesis with Self-rectified Pseudo-StereoInternational Journal of Computer Vision10.1007/s11263-023-01803-z131:8(2032-2043)Online publication date: 10-May-2023
https://dl.acm.org/doi/10.1007/s11263-023-01803-z
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