Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jin-Kee Chae

dblp:168/7837 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › video frame interpolation › interpolation › image interpolation
cubic convolution interpolation
0.212015
Cubic Convolution Scaler Optimized for Local Property of Image Data · IEEE Trans. Image Process. 2015
Image and video processing › video frame interpolation › interpolation
image interpolation
0.212015
Cubic Convolution Scaler Optimized for Local Property of Image Data · IEEE Trans. Image Process. 2015
Image and video processing › image resampling
image rescaling
0.212015
Cubic Convolution Scaler Optimized for Local Property of Image Data · IEEE Trans. Image Process. 2015
YearPublicationVenuePosition
2015 Cubic Convolution Scaler Optimized for Local Property of Image Data
abstract
A scaler is one of the most important modules in various video applications, such as ultra-high definition TV and scalable video systems. A variety of scaling techniques have been used to increase the video quality when the resolution of the source image has to be up- and down-scaled. Some conventional schemes exploit the property of local block data. Others consider the edge information of the data to be scaled. In this paper, we formulate a scaling problem to minimize the information loss resulting from the resizing process. The loss is considered in both the spatial and the frequency domains, and then it is minimized to optimize the kernel of the scaler. The simulation results show that the proposed algorithm reduces the information loss more than conventional schemes. When compared with the conventional algorithms, the proposed method outperforms those with similar complexity.
Jin-Kee Chae, Jae-Yung Lee, Min-Ho Lee, Jong-Ki Han, Truong Q. Nguyen, Woon-Young Yeo
IEEE Trans. Image Process.1