Full-reference image quality assessment by combining global and local\n distortion measures
Bibliographic record
Abstract
Full-reference image quality assessment (FR-IQA) techniques compare a\nreference and a distorted/test image and predict the perceptual quality of the\ntest image in terms of a scalar value representing an objective score. The\nevaluation of FR-IQA techniques is carried out by comparing the objective\nscores from the techniques with the subjective scores (obtained from human\nobservers) provided in the image databases used for the IQA. Hence, we\nreasonably assume that the goal of a human observer is to rate the distortion\npresent in the test image. The goal oriented tasks are processed by the human\nvisual system (HVS) through top-down processing which actively searches for\nlocal distortions driven by the goal. Therefore local distortion measures in an\nimage are important for the top-down processing. At the same time, bottom-up\nprocessing also takes place signifying spontaneous visual functions in the HVS.\nTo account for this, global perceptual features can be used. Therefore, we\nhypothesize that the resulting objective score for an image can be derived from\nthe combination of local and global distortion measures calculated from the\nreference and test images. We calculate the local distortion by measuring the\nlocal correlation differences from the gradient and contrast information. For\nglobal distortion, dissimilarity of the saliency maps computed from a bottom-up\nmodel of saliency is used. The motivation behind the proposed approach has been\nthoroughly discussed, accompanied by an intuitive analysis. Finally,\nexperiments are conducted in six benchmark databases suggesting the\neffectiveness of the proposed approach that achieves competitive performance\nwith the state-of-the-art methods providing an improvement in the overall\nperformance.\n
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".