Abstract
This paper introduces a novel content-adaptive image downscaling method. The key idea
is to optimize the shape and locations of the downsampling kernels to better align with local
image features. Our content-adaptive kernels are formed as a bilateral combination of two
Gaussian kernels defined over space and color, respectively. This yields a continuum ranging
from smoothing to edge/detail preserving kernels driven by image content. We optimize these
kernels to represent the input image well, by finding an output image from which the input
can be well reconstructed. This is technically realized as an iterative maximum-likelihood
optimization using a constrained variation of the Expectation-Maximization algorithm. In
comparison to previous downscaling algorithms, our results remain crisper without suffering
from ringing artifacts. Besides natural images, our algorithm is also effective for creating
pixel art images from vector graphics inputs, due to its ability to keep linear
features sharp and connected.
@article{Kopf2013,
author = {Johannes Kopf and Ariel Shamir and Pieter Peers},
title = {Content-Adaptive Image Downscaling},
journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2013)},
year = {2013},
volume = {32},
number = {6},
pages = {to appear}
}
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