Blind Stereo Quality Assessment Based on Learned Features From Binocular Combined Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quality assessment of stereo images confronts more challenges than its 2D counterparts. Direct use of 2D assessment methods is not sufficient to deal with the challenges of 3D perception. In this paper, an efficient general-purpose no-reference stereo image quality assessment, based on unsupervised feature learning, is presented. The proposed method extracts features without any prior knowledge about the types and levels of distortions. This property enables our method to be adaptable for different applications. The perceived contrast and phase of the binocular combination of original stereo images are utilized to learn individual dictionaries. For each distorted stereo image, two feature vectors are pooled, in a hierarchical manner, over all sparse representation vectors of phase and contrast blocks by their corresponding dictionaries. Performance results of learning a regression model by the features acknowledge the superiority of the proposed method to state-of-the-art algorithms.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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