Deep image compression based on multi-scale deformable convolution

Daowen Li, Yingming Li, Heming Sun, Lu Yu*

*この研究の対応する著者

研究成果: Article査読

抄録

Deep image compression efficiency has been improved in the past years. However, to fully exploit context information for compressing image objects of different scales and shapes, more adaptive geometric structure of inputs should be considered. In this paper, we novelly introduce deformable convolution and its spatial attention extension into deep image compression task to fully exploit the context information. Specifically, a novel deep image compression network with Multi-Scale Deformable Convolution and Spatial Attention, named MS-DCSA, is proposed to better extract compact and efficient latent representation as well as reconstruct higher-quality images. First, multi-scale deformable convolution is presented to provide multi-scale receptive fields for learning spatial sampling offsets in deformable operations. Subsequently, multi-scale deformable spatial attention module is developed to generate attention masks to re-weight extracted features according to their importance. In addition, the multi-scale deformable convolution is applied to design delicate up/down sampling modules. Extensive experiments demonstrate that the proposed MS-DCSA network achieves improved performance on both PSNR and MS-SSIM quality metrics, compared to conventional as well as competing deep image compression methods.

本文言語English
論文番号103573
ジャーナルJournal of Visual Communication and Image Representation
87
DOI
出版ステータスPublished - 2022 8月

ASJC Scopus subject areas

  • 信号処理
  • メディア記述
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

フィンガープリント

「Deep image compression based on multi-scale deformable convolution」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル