Optimizing landmark insertions for interactive light field streaming
Bibliographic record
Abstract
Light field imaging enables a user to navigate and observe a static 3D scene from different viewpoints. Downloading the entire data prior to navigation would incur a large startup delay. Instead, previous works propose an interactive light field streaming (ILFS) framework, where a user periodically requests a viewpoint, and in response the server transmits a presynthesized and encoded viewpoint image. Using I-frame, P-frame and previously proposed merge frame that facilitates view-switches, the challenge is how to design and pre-encode a storage-constrained frame structure to enable efficient view navigation. In this paper, we initialize “landmarks” into a structure to improve ILFS performance. A landmark is a designated view with P-frames to/from each neighborhood view, so that any viewpoint image can transition to any other viewpoint image by first visiting a landmark, and then from the landmark to the destination view. This results in a transmission cost of only two P-frames. Using a Lloyd's algorithm variant, we first incrementally insert into a frame structure landmarks one at a time at locally optimal locations. We then employ a greedy algorithm to add / subtract P-frames based on a rate-storage criterion. Experimental results show that our proposed structures have noticeably lower expected transmission cost for the same storage than structures generated by a previous greedy algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".