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Bizheng Luo

dblp:418/4670 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

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

Computer networks · 1 · 1 since 2021

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.

Theoretical computer science
1 paper
Coding theory · 100%
Computer graphics and multimedia
1 paper
Image and video coding · 100%

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

TopicWeightPapersLastEvidence papers
Image and video coding
scalable video coding
1.012026
Performance Bound for Online Scalable Video Coding and Scalable Semantic Coding · IEEE Trans. Mob. Comput. 2026
Coding theory › error-correcting codes › coding bounds
coding gain bound
1.012026
Performance Bound for Online Scalable Video Coding and Scalable Semantic Coding · IEEE Trans. Mob. Comput. 2026
Coding theory › source coding
rate-distortion theory
1.012026
Performance Bound for Online Scalable Video Coding and Scalable Semantic Coding · IEEE Trans. Mob. Comput. 2026

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

mutual information analysis · 2.0entropy analysis · 2.0
YearPublicationVenuePosition
2026 Performance Bound for Online Scalable Video Coding and Scalable Semantic Coding
abstract
Recently, scalable video coding (SVC) and scalable semantic coding (SSC) have emerged as effective solutions for supporting online video streaming in applications such as remote cockpits and telemedicine. Although the significance of SVC and SSC is well established, the underlying principles related to their performance bounds have not been thoroughly investigated. To address this issue, we study the performance bounds for online SVC/SSC in this paper. Initially, to evaluate coding performance, we propose a new metric referred to as effective coding gain (ECG), which jointly considers the entropy of the source data and the mutual information between the source and the encoded video data. Next, from the perspective of information theory, we derive a closed-form expression for the ECG bound while accounting for the impacts of diverse SVC/SSC coding structures. Our results not only ensure that the performance bounds can be efficiently evaluated but also provide valuable insights for resource allocation, cost optimization, performance evaluation and comparison, as well as for understanding the optimization space of existing systems.
Weijia Han, Bizheng Luo, Wei Teng, Xiao Ma 0007, Chuan Huang 0001
IEEE Trans. Mob. Comput.3