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

Image compression techniques typically focus on compressing rectangularimages for human consumption, however, resulting in transmitting redundantcontent for downstream applications. To overcome this limitation, some previousworks propose to semantically structure the bitstream, which can meet specificapplication requirements by selective transmission and reconstruction.Nevertheless, they divide the input image into multiple rectangular regionsaccording to semantics and ignore avoiding information interaction among them,causing waste of bitrate and distorted reconstruction of region boundaries. Inthis paper, we propose to decouple an image into multiple groups with irregularshapes based on a customized group mask and compress them independently. Ourgroup mask describes the image at a finer granularity, enabling significantbitrate saving by reducing the transmission of redundant content. Moreover, toensure the fidelity of selective reconstruction, this paper proposes theconcept of group-independent transform that maintain the independence amongdistinct groups. And we instantiate it by the proposed Group-IndependentSwin-Block (GI Swin-Block). Experimental results demonstrate that our frameworkstructures the bitstream with negligible cost, and exhibits superiorperformance on both visual quality and intelligent task supporting.

Semantically Structured Image Compression via Irregular Group-Based Decoupling