Hierarchical Independent Coding Scheme for Varifocal Multiview Images Based on Angular-Focal Joint Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Varifocal multiview (VFMV) images are dense views that focus on variable focal planes. Thus, VFMV images are highly redundant in the angular, spatial and focal dimensions. In this article, the redundancies of VFMV images are analyzed and represented by full parallaxes and focal inconsistency. To exploit these distinctive redundancies, we propose a hierarchical independent coding scheme based on angular-focal joint prediction. The scheme is constructed by hierarchical independent prediction structure (HIPS) and angular-focal joint prediction (AFJP). The HIPS separates all views into several independent subdivisions and assigns different hierarchies inside each subdivision, which enhances random access capability and scalability. The AFJP conducts motion estimation and focal approximation simultaneously to predict parallaxes and focal inconsistency. Therefore, the redundancies in the angular and focal dimensions can be exploited by the proposed coding scheme. We construct a VFMV dataset with 10 test sequences for different acquisition methods. The experimental results on these test sequences demonstrate that the proposed scheme outperforms all comparison schemes in objective quality, subjective quality and random access capability. Specifically, the proposed coding scheme achieves up to 2.661 dB PSNR gains and 52.817% bitrate savings compared with the HEVC random access benchmark scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it