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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Most of the existing deep learning based end-to-end video coding (DLEC)architectures are designed specifically for RGB color format, yet the videocoding standards, including H.264/AVC, H.265/HEVC and H.266/VVC developed overpast few decades, have been designed primarily for YUV 4:2:0 format, where thechrominance (U and V) components are subsampled to achieve superior compressionperformances considering the human visual system. While a broad number ofpapers on DLEC compare these two distinct coding schemes in RGB domain, it isideal to have a common evaluation framework in YUV 4:2:0 domain for a more faircomparison. This paper introduces a new DLEC architecture for video coding toeffectively support YUV 4:2:0 and compares its performance against the HEVCstandard under a common evaluation framework. The experimental results on YUV4:2:0 video sequences show that the proposed architecture can outperform HEVCin intra-frame coding, however inter-frame coding is not as efficient oncontrary to the RGB coding results reported in recent papers.

A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space