Effective Content-Aware Chroma Reconstruction Method for Screen Content Images

In this paper, we propose an effective novel content-aware chroma reconstruction (CACR) method for screen content images (SCIs). After receiving the decoded downsampled YUV image on the client side, our fast chroma-copy approach reconstructs the missing chroma pixels in the flat regions of SCI. Then...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 28(2019), 3 vom: 10. März, Seite 1108-1117
1. Verfasser: Chung, Kuo-Liang (VerfasserIn)
Weitere Verfasser: Liang, Yan-Cheng, Wang, Ching-Sheng
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:In this paper, we propose an effective novel content-aware chroma reconstruction (CACR) method for screen content images (SCIs). After receiving the decoded downsampled YUV image on the client side, our fast chroma-copy approach reconstructs the missing chroma pixels in the flat regions of SCI. Then, for non-flat regions, a non-flat region-based winner-first voting (NRWV) strategy is proposed to identify the chroma subsampling scheme used on the server side prior to compression. Further, an effective adaptive hybrid approach is proposed to reconstruct each missing chroma pixel in the non-flat region by fusing the two reconstructed results, one from our modified NRWV-based chroma subsampling-binding and luma-guided chroma reconstruction scheme, which favors the sharp edges in SCI, as well as the other from the bicubic interpolation scheme, which favors blurred and continuous-tone textures. Further, based on the identified chroma subsampling scheme, a geometry alignment-based error compensation approach is proposed to enhance the reconstructed chroma image. Based on typical test SCIs and JCT-VC screen content videos, comprehensive experiments are carried out in HEVC-16.17 to demonstrate that in terms of quality, visual effect, and quality-bitrate tradeoff of the reconstructed SCIs, our CACR method significantly outperforms the existing state-of-the-art methods
Beschreibung:Date Completed 30.10.2018
Date Revised 30.10.2018
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1941-0042
DOI:10.1109/TIP.2018.2875340