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
Demo Data from EDBT 2026 / reviewers in the wild / expert

J. Y. Yen

dblp:83/603 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 2

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 coding · 67% Image and video processing · 33%

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

TopicWeightPapersLastEvidence papers
Image and video coding › quantization › vector quantization
classified vector quantization		 0.011998
Inverse error-diffusion using classified vector quantization · IEEE Trans. Image Process. 1998
Image and video processing › image restoration › inverse problem
inverse halftoning		 0.011998
Inverse error-diffusion using classified vector quantization · IEEE Trans. Image Process. 1998
Image and video coding › quantization
vector quantization		 0.011998
Inverse error-diffusion using classified vector quantization · IEEE Trans. Image Process. 1998

Methods — techniques the papers use, named apart from their topics

codebook design · 0.0CVQ encoding/decoding · 0.0
YearPublicationVenuePosition
1998 Inverse Halftoning of Color Images Using Classified Vector Quantization
Jim Z. C. Lai, J. Y. Yen
 J. Vis. Commun. Image Represent.2
1998 Inverse error-diffusion using classified vector quantization
abstract
This correspondence extends and modifies classified vector quantization (CVQ) to solve the problem of inverse halftoning. The proposed process consists of two phases: the encoding phase and decoding phase. The encoding procedure needs a codebook for the encoder which transforms a halftoned image to a set of codeword-indices. The decoding process also requires a different codebook for the decoder which reconstructs a gray-scale image from a set of codeword-indices. Using CVQ, the reconstructed gray-scale image is stored in compressed form and no further compression may be required. This is different from the existing algorithms, which reconstructed a halftoned image in an uncompressed form. The bit rate of encoding a reconstructed image is about 0.51 b/pixel.
Jim Z. C. Lai, J. Y. Yen
 IEEE Trans. Image Process.2