Tser-Tzi Tang

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dblp:138/6744 · DBLP profile ↗

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1ranked-venue papers

0as first author

0since 2021 · last 1994

—ORCID · none

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

Computer networks · 1

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 · 100%
Artificial intelligence 1 paper		Representation and self-supervised learning · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Representation and self-supervised learning › prototype learning self-organizing map	1	1994	Vector quantization using tree-structured self-organizing feature maps · IEEE J. Sel. Areas Commun. 1994
Image and video coding › quantization › vector quantization tree-structured vector quantization	1	1994	Vector quantization using tree-structured self-organizing feature maps · IEEE J. Sel. Areas Commun. 1994
Image and video coding › quantization vector quantization	1	1994	Vector quantization using tree-structured self-organizing feature maps · IEEE J. Sel. Areas Commun. 1994
Image and video coding › quantization › vector quantization codebook design	1	1994	Vector quantization using tree-structured self-organizing feature maps · IEEE J. Sel. Areas Commun. 1994

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

vector quantization · 0.0self-organizing feature map · 0.0

Year	Publication	Venue	Position
1994	Vector quantization using tree-structured self-organizing feature maps abstract In this paper, we propose a binary-tree structure neural network model suitable for structured clustering. During and after training, the centroids of the clusters in this model always form a binary tree in the input pattern space. This model is used to design tree search vector quantization codebooks for image coding. Simulation results show that the acquired codebook not only produces better-quality images but also achieves a higher compression ratio than conventional tree search vector quantization. When source coding is applied after VQ, the new model performs better than the generalized Lloyd algorithm in terms of distortion, bits per pixel, and encoding complexity for low-detail and medium-detail images.> Tzi-Dar Chiueh, Tser-Tzi Tang, Liang-Gee Chen	IEEE J. Sel. Areas Commun.	2