A well-perceived, blind image quality assessment algorithm using an enhanced noise feature criterion
Yi-Pin Hsu, Chuan-Yen Hsiao · 2026
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Description
Yi-Pin Hsu and Chuan-Yen Hsiao offer a lean method to grade image quality the way people see it, in a 2026 paper. Blind quality grading judges a photo with no clean original to compare, a need in cameras and streaming. Deep learning graders score well but demand big networks and large training sets.
Their method builds on noise features — how grain in a picture betrays damage — with fixes for a known weak spot. Old noise analysis wobbled on images with strong sideways patterns, like fences. New weighting and decision rules steady those cases. Tests on four public image sets matched or beat the older method's agreement with human scores. A light grader can run inside devices rather than in the cloud.
Catalog record
| CREATOR | Yi-Pin Hsu, Chuan-Yen Hsiao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |