Video Stippling

11/28/2010
by   Thomas Houit, et al.
0

In this paper, we consider rendering color videos using a non-photo-realistic art form technique commonly called stippling. Stippling is the art of rendering images using point sets, possibly with various attributes like sizes, elementary shapes, and colors. Producing nice stippling is attractive not only for the sake of image depiction but also because it yields a compact vectorial format for storing the semantic information of media. Moreover, stippling is by construction easily tunable to various device resolutions without suffering from bitmap sampling artifacts when resizing. The underlying core technique for stippling images is to compute a centroidal Voronoi tessellation on a well-designed underlying density. This density relates to the image content, and is used to compute a weighted Voronoi diagram. By considering videos as image sequences and initializing properly the stippling of one image by the result of its predecessor, one avoids undesirable point flickering artifacts and can produce stippled videos that nevertheless still exhibit noticeable artifacts. To overcome this, our method improves over the naive scheme by considering dynamic point creation and deletion according to the current scene semantic complexity, and show how to effectively vectorize video while adjusting for both color and contrast characteristics. Furthermore, we explain how to produce high quality stippled "videos" (eg., fully dynamic spatio-temporal point sets) for media containing various fading effects, like quick motions of objects or progressive shot changes. We report on practical performances of our implementation, and present several stippled video results rendered on-the-fly using our viewer that allows both spatio-temporal dynamic rescaling (eg., upscale vectorially frame rate).

READ FULL TEXT

page 5

page 6

page 8

page 9

page 11

page 12

research
05/10/2019

Spatio-temporal Video Re-localization by Warp LSTM

The need for efficiently finding the video content a user wants is incre...
research
01/03/2023

Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment

Compressed videos often exhibit visually annoying artifacts, known as Pe...
research
08/09/2018

Deep Video Color Propagation

Traditional approaches for color propagation in videos rely on some form...
research
05/19/2023

PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction

In this paper, we investigate the challenge of spatio-temporal video pre...
research
12/05/2022

BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization

How to effectively explore the colors of reference exemplars and propaga...
research
09/29/2017

Photometric Stabilization for Fast-forward Videos

Videos captured by consumer cameras often exhibit temporal variations in...
research
07/21/2009

Image Sampling with Quasicrystals

We investigate the use of quasicrystals in image sampling. Quasicrystals...

Please sign up or login with your details

Forgot password? Click here to reset