English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a deep learning system for attention-guided dual-layer imagecompression (AGDL). In the AGDL compression system, an image is encoded intotwo layers, a base layer and an attention-guided refinement layer. Unlike theexisting ROI image compression methods that spend an extra bit budget equallyon all pixels in ROI, AGDL employs a CNN module to predict those pixels on andnear a saliency sketch within ROI that are critical to perceptual quality. Onlythe critical pixels are further sampled by compressive sensing (CS) to form avery compact refinement layer. Another novel CNN method is developed to jointlydecode the two compression layers for a much refined reconstruction, whilestrictly satisfying the transmitted CS constraints on perceptually criticalpixels. Extensive experiments demonstrate that the proposed AGDL systemadvances the state of the art in perception-aware image compression.

Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton