45,268 research outputs found
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In
these media, dynamic and still elements are juxtaposed to create an artistic
and narrative experience. Creating a high-quality, aesthetically pleasing
cinemagraph requires isolating objects in a semantically meaningful way and
then selecting good start times and looping periods for those objects to
minimize visual artifacts (such a tearing). To achieve this, we present a new
technique that uses object recognition and semantic segmentation as part of an
optimization method to automatically create cinemagraphs from videos that are
both visually appealing and semantically meaningful. Given a scene with
multiple objects, there are many cinemagraphs one could create. Our method
evaluates these multiple candidates and presents the best one, as determined by
a model trained to predict human preferences in a collaborative way. We
demonstrate the effectiveness of our approach with multiple results and a user
study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary
materia
Learning Convolutional Networks for Content-weighted Image Compression
Lossy image compression is generally formulated as a joint rate-distortion
optimization to learn encoder, quantizer, and decoder. However, the quantizer
is non-differentiable, and discrete entropy estimation usually is required for
rate control. These make it very challenging to develop a convolutional network
(CNN)-based image compression system. In this paper, motivated by that the
local information content is spatially variant in an image, we suggest that the
bit rate of the different parts of the image should be adapted to local
content. And the content aware bit rate is allocated under the guidance of a
content-weighted importance map. Thus, the sum of the importance map can serve
as a continuous alternative of discrete entropy estimation to control
compression rate. And binarizer is adopted to quantize the output of encoder
due to the binarization scheme is also directly defined by the importance map.
Furthermore, a proxy function is introduced for binary operation in backward
propagation to make it differentiable. Therefore, the encoder, decoder,
binarizer and importance map can be jointly optimized in an end-to-end manner
by using a subset of the ImageNet database. In low bit rate image compression,
experiments show that our system significantly outperforms JPEG and JPEG 2000
by structural similarity (SSIM) index, and can produce the much better visual
result with sharp edges, rich textures, and fewer artifacts
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