Screentone-Preserved Manga Retargeting

Abstract

As a popular comic style, manga offers a unique impression by utilizing a rich set ofbitonal patterns, or screentones, for illustration. However, screentones can easily be degraded when manga is resized in terms of aspect ratio and resolution for manga re-layout and e-manga migration applications. To tackle this problem, we propose the first automatic manga retargeting method that synthesizes a retargeted manga image while preserving the prominent structure and fine screentone intended by the manga artist. While modern natural photo retargeting methods can achieve prominent structure preservation, preserving screentones within arbitrarily shaped regions is very challenging due to two properties of manga: (i) pattern constancy under translation, and (ii) non-compatibility with interpolation. To circumvent this barrier, we propose learning a quantized representation of screentones that is translation-invariant and pointwisely representable through a tailored manga reconstruction network with a screentone-anchored codebook. Thanks to these merits, we can perform the re-synthesis operation using existing photo retargeting methods and achieve the desired manga retargeting results. We conducted extensive qualitative and quantitative experiments to validate the effectiveness of our method, and we achieved notably compelling results compared to alternative methods.