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/

Zendy Plus

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

Zendy Tools

Zendy Open

The past few years have witnessed fast development in video qualityenhancement via deep learning. Existing methods mainly focus on enhancing theobjective quality of compressed video while ignoring its perceptual quality. Inthis paper, we focus on enhancing the perceptual quality of compressed video.Our main observation is that enhancing the perceptual quality mostly relies onrecovering high-frequency sub-bands in wavelet domain. Accordingly, we proposea novel generative adversarial network (GAN) based on multi-level waveletpacket transform (WPT) to enhance the perceptual quality of compressed video,which is called multi-level wavelet-based GAN (MW-GAN). In MW-GAN, we firstapply motion compensation with a pyramid architecture to obtain temporalinformation. Then, we propose a wavelet reconstruction network withwavelet-dense residual blocks (WDRB) to recover the high-frequency details. Inaddition, the adversarial loss of MW-GAN is added via WPT to further encouragehigh-frequency details recovery for video frames. Experimental resultsdemonstrate the superiority of our method.

Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video