DCID: A divide and conquer approach to solving the trade-off problem between artifacts caused by enhancement procedure in image downscaling

Eun Su Kang, Yeon Jeong Chae, Jae Hyeon Park, Sung In Cho

Research output: Contribution to journalArticlepeer-review

Abstract

Conventional research on image downscaling is conducted to improve the visual quality of the resultant downscaled image. However, there is an intractable problem, a trade-off relationship between artifacts such as aliasing and ringing, caused by enhancement procedure in image downscaling. To solve this problem, we propose a novel method that applies a divide-and-conquer approach for image downscaling (DCID). Specifically, the proposed DCID includes Weight-Net for dividing regions into enhancement first and artifact-least first regions and two generators that are optimized for divided regions to conquer the trade-off problem in the image downscaling task. The proposed method can generate a downscaled image without creating artifacts while preserving the perceptual quality of the input image. In objective and subjective evaluations, our experimental results show that the quality of the downscaled images generated by the proposed DCID is significantly better than benchmark methods.

Original languageEnglish
Article number117133
JournalSignal Processing: Image Communication
Volume126
DOIs
StatePublished - Aug 2024

Keywords

  • Artifact-least
  • Deep neural network
  • Image downscaling

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