Visually Favorable Tone-Mapping With High Compression Performance in Bit-Depth Scalable Video Coding
Why this work is in the frame
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Bibliographic record
Abstract
In bit-depth scalable video coding, the tone-mapping scheme used to convert high-bit-depth to eight-bit videos is an essential yet very often ignored component. In this paper, we demonstrate that an appropriate choice of a tone-mapping operator can improve the coding efficiency of bit-depth scalable encoders. We present a new tone-mapping scheme that delivers superior compression efficiency while adhering to a predefined base layer perceptual quality. We develop numerical models that estimate the base layer bit-rate (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> ), the enhancement layer bitrate (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ), and the mismatch (Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> ) between the resulting low dynamic range (LDR) base-layer signal and the predefined base layer representation. Our proposed tone curve is given by the solution of an optimization problem which minimizes a weighted sum of R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> , R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> , and Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> . The problem formulation also considers the temporal effect of tone-mapping by adding a constraint to the optimization problem that suppresses flickering artifacts. We also propose a technique with which to tone-map a high-bit-depth video directly in a compression-friendly color space (e.g., one luma and two chroma channels) without converting to the RGB domain. Experimental results show that we can save up to 40% of the total bit-rate (or 3.5 dB PSNR improvement for the same bitrate), and, in general, about 20% bit-rate savings can be achieved.
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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 | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 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