Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 161

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Assessing Visual Quality of Omnidirectional Videos

Open access
Xu, Mai, Li, Chen, Chen, Zhenzhong, Wang, Zulin, Guan, Zhenyu·2019·IEEE Transactions on Circuits and Systems for Video Technology

Abstract

In contrast with traditional videos, omnidirectional videos enable spherical viewing direction with support for head-mounted displays, providing an interactive and immersive experience. Unfortunately, to the best of our knowledge, there are only a few visual quality assessment (VQA) methods, either subjective or objective, for omnidirectional video coding. This paper proposes both subjective and objective methods for assessing the quality loss in encoding an omnidirectional video. Specifically, we first present a new database, which includes the viewing direction data from several subjects watching omnidirectional video sequences. Then, from our database, we find a high consistency in viewing directions across different subjects. The viewing directions are normally distributed in the center of the front regions, but they sometimes fall into other regions, related to the video content. Given this finding, we present a subjective VQA method for measuring the difference mean opinion score (DMOS) of the whole and regional omnidirectional video, in terms of overall DMOS and vectorized DMOS, respectively. Moreover, we propose two objective VQA methods for the encoded omnidirectional video, in light of the human perception characteristics of the omnidirectional video. One method weighs the distortion of pixels with regard to their distances to the center of front regions, which considers human preference in a panorama. The other method predicts viewing directions according to the video content, and then the predicted viewing directions are leveraged to allocate weights to the distortion of each pixel in our objective VQA method. Finally, our experimental results verify that both the subjective and objective methods proposed in this paper advance the state-of-the-art VQA for omnidirectional videos.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tcsvt.2018.2886277

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 161

Metadata

Authors
Xu, M., Li, C., Chen, Z., Wang, Z., & Guan, Z.
Venue
IEEE Transactions on Circuits and Systems for Video Technology
Published
2019-12
Volume
29
Issue
12
Pages
3516-3530
ISSN
  • 1051-8215 (ISSN-L)
  • 1558-2205
OpenAlex
W2890110575
Cited by
161
References
2
FWCI
12.59
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Assessing Visual Quality of Omnidirectional Videos. https://app.cochat.ai/papers/10.1109/tcsvt.2018.2886277
The paper
Xu, M., Li, C., Chen, Z., Wang, Z., & Guan, Z. (2019). Assessing Visual Quality of Omnidirectional Videos. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2018.2886277

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.