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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Depth-image-based rendering (DIBR) is a commonly used method for synthesizing additional views using video-plus-depth (V+D) format. A critical issue with DIBR based view synthesis is the lack of information behind foreground objects. This lack is manifested as disocclusions, holes, next to the foreground objects in rendered virtual views as a consequence of the virtual camera “seeing” behind the foreground object. The disocclusions are larger in the extrapolation case, i.e. the single camera case. Texture synthesis methods (inpainting methods) aim to fill these disocclusions by producing plausible texture content. However, virtual views inevitably exhibit both spatial and temporal inconsistencies at the filled disocclusion areas, depending on the scene content. In this paper we propose a layered depth image (LDI) approach that improves the spatio-temporal consistency. In the process of LDI generation, depth information is used to classify the foreground and background in order to form a static scene sprite from a set of neighboring frames. Occlusions in the LDI are then identified and filled using inpainting, such that no disocclusions appear when the LDI data is rendered to a virtual view. In addition to the depth information, optical flow is computed to extract the stationary parts of the scene and to classify the occlusions in the inpainting process. Experimental results demonstrate that spatio-temporal inconsistencies are significantly reduced using the proposed method. Furthermore, subjective and objective qualities are improved compared to state-of-the-art reference methods

Spatio-temporal consistent depth-image-based rendering using layered depth image and inpainting