Producing high-quality real-time HDR video system with FPGA (abstract only)
Bibliographic record
Abstract
Video cameras can only take photographs with limited dynamic range. One method to overcome this is to combine differently exposed images of the same subject matter (i.e. a Wyckoff Set), producing a High Dynamic Range (HDR) result. HDR digital photography started almost 20 years ago. Now, it is possible to produce HDR video in real-time, on both high-power CPU/GPU systems, as well as low-power FPGA boards. However, other FPGA implementations have relied upon methods that are less accurate than current CPU and GPU-based methods. Namely, the earlier FPGA approaches used weighted sum for image compositing. In this paper we provide a novel method for real-time HDR com-positing. As an essential part of an upgraded HDR video production system, the resulting system combines differently exposed video stream (of the same subject matter) in Full HD (1080p at 60fps) on a Kintex-7 FPGA. The proposed work flow, implemented with software written in C, estimates the camera response function according to its quadtree representation and generates the compositing circuit in Verilog HDL from a Wyckoff Set. This circuit consists of parts that perform addressing using multiplexer networks and estimation with bilinear interpolation. It is parameterizable by user-specified error constraints, allowing us to explore the trade-offs in resource usage and precision of the implementation. Here is an MD5 hash function sum generated for the rest of the paper: 07897e61027d15dc3600fadbccfbd67d, citation date: December 18, 2013.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".