Demo Data from SIGIR 2026 / reviewers in the wild / expert
Goce Dimitrov
dblp:282/5935
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—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 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.
| Computer graphics and multimedia
1 paper |
Multimedia systems and quality of experience · 60% Image and video coding · 40% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia systems and quality of experience
subjective quality assessment |
0.5 | 1 | 2021 | BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality Assessment · IEEE Trans. Multim. 2021 |
Image and video coding
texture compression |
0.5 | 1 | 2021 | BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality Assessment · IEEE Trans. Multim. 2021 |
Image and video coding
video compression |
0.5 | 1 | 2021 | BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality Assessment · IEEE Trans. Multim. 2021 |
Multimedia systems and quality of experience
video quality assessment |
0.5 | 1 | 2021 | BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality Assessment · IEEE Trans. Multim. 2021 |
Methods — techniques the papers use, named apart from their topics
subjective evaluation · 0.5objective analysis · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality AssessmentabstractHighly textured video content is challenging to compress since the bit-rate to video quality trade-off is high and complex perceptual masking influences performance. Test datasets that cover a wide range of texture types are thus important for codec evaluation, but few exist. In order to study the properties of video texture, this paper introduces a Synthetic video Texture dataset (BVI-SynTex) that was generated using a Computer-Generated Imagery (CGI) environment. It contains 196 sequences clustered in three different texture types and offers the capability of being able to generate many versions of the same scene with different video parameters. It therefore provides a flexible basis for studying the influence of texture type and parameters on video compression and perceived video quality. A thorough validation and comparison of BVI-SynTex with similarly textured natural video content is performed. The comparisons show that BVI-SynTex exhibits a comparable coverage over the spatial and temporal domain and that it produces similar encoding statistics to real video datasets. A subset of the BVI-SynTex dataset was selected to perform a subjective evaluation of compression using the MPEG HEVC codec.The results show the impact of the content parameters to both the compression efficiency and the perceived quality. The publicly available BVI-SynTex dataset contains all source sequences, the objective and subjective analysis results, providing a valuable resource for the research community. Angeliki V. Katsenou, Goce Dimitrov, David Bull 0001 |
IEEE Trans. Multim. | 2 |