Reduced reference image quality assessment using Principal Component Analysis
Bibliographic record
Abstract
With the tendency of converging services delivered on wired and wireless networks, the consumer expectancy includes live video streaming. In this context, the objective image and video quality assessment becomes an essential but a challenging requirement. With the rapid evolution of the wireless video applications, the continuation of the Quality of Service (QoS) is a key paradigm for the roll-out of these services, which demands for an efficient quality evaluator for the dynamic monitoring and parameter setting of the digital video system. In this paper, we propose a Reduced-Reference (RR) image quality assessment metric based on the Principal Component Analysis (PCA). In our metric, the transformed data set is obtained which represents the original data solely in terms of the eigenvectors we choose, giving us the most efficient expression of the data. The mean gradient values are calculated from the transformed data using edge detection methods based on the Sobel-operator. We define a Quality Index that measures the difference between our metric's values computed on the transmitted and received images, respectively. The experimental results show that our RR-PCA proposed metric correlates well with the subjective quality scores.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".