Perspectives on the definition of visually lossless quality for mobile and large format displays
Bibliographic record
Abstract
Advances in imaging and display engineering have given rise to new and improved image and video applications that aim to maximize visual quality under given resource constraints (e.g., power, bandwidth). Because the human visual system is an imperfect sensor, the images/videos can be represented in a mathematically lossy fashion but with enough fidelity that the losses are visually imperceptible—commonly termed “visually lossless.” Although a great deal of research has focused on gaining a better understanding of the limits of human vision when viewing natural images/video, a universally or even largely accepted definition of visually lossless remains elusive. Differences in testing methodologies, research objectives, and target applications have led to multiple ad-hoc definitions that are often difficult to compare to or otherwise employ in other settings. We present a compendium of technical experiments relating to both vision science and visual quality testing that together explore the research and business perspectives of visually lossless image quality, as well as review recent scientific advances. Together, the studies presented in this paper suggest that a single definition of visually lossless quality might not be appropriate; rather, a better goal would be to establish varying levels of visually lossless quality that can be quantified in terms of the testing paradigm.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".