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

Lossy compression algorithms are typically designed to achieve the lowestpossible distortion at a given bit rate. However, recent studies show thatpursuing high perceptual quality would lead to increase of the lowestachievable distortion (e.g., MSE). This paper provides nontrivial resultstheoretically revealing that, \textit{1}) the cost of achieving perfectperception quality is exactly a doubling of the lowest achievable MSEdistortion, \textit{2}) an optimal encoder for the "classic" rate-distortionproblem is also optimal for the perceptual compression problem, \textit{3})distortion loss is unnecessary for training a perceptual decoder. Further, wepropose a novel training framework to achieve the lowest MSE distortion underperfect perception constraint at a given bit rate. This framework uses a GANwith discriminator conditioned on an MSE-optimized encoder, which is superiorover the traditional framework using distortion plus adversarial loss.Experiments are provided to verify the theoretical finding and demonstrate thesuperiority of the proposed training framework.

On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework