An Overview of the MPEG Standard for Storage and Transport of Visual Volumetric Video-Based Coding
Bibliographic record
Abstract
The increasing popularity of virtual, augmented, and mixed reality (VR/AR/MR) applications is driving the media industry to explore the creation and delivery of new immersive experiences. One of the trends is volumetric video, which allows users to explore content unconstrained by the traditional two-dimensional window of director’s view. The ISO/IEC joint technical committee 1 subcommittee 29, better known as the Moving Pictures Experts Group (MPEG), has recently finalized a group of standards, under the umbrella of Visual Volumetric Video-based Coding (V3C). These standards aim to efficiently code, store, and transport immersive content with 6 degrees of freedom. The V3C family of standards currently consists of three documents: 1) ISO/IEC 23090-5 defines the generic concepts of volumetric video-based coding and its application to dynamic point cloud data; 2) ISO/IEC 23090-12 specifies another application that enables compression of volumetric video content captured by multiple cameras; and 3) ISO/IEC 23090-10 describes how to store and deliver V3C compressed volumetric video content. Each standard leverages the capabilities of traditional 2D video coding and delivery solutions, allowing for re-use of existing infrastructures which facilitates fast deployment of volumetric video. This article provides an overview of the generic concepts of V3C, as defined in ISO/IEC 23090-5. Furthermore, it describes V3C carriage related functionalities specified in ISO/IEC 23090-10 and offers best practices for the community with respect to storage and delivery of volumetric video.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".