CNN-based Cross-dataset No-reference Image Quality Assessment
Tutkimustuotos › › vertaisarvioitu
Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2019 International Conference on Computer Vision Workshop, ICCVW 2019 |
| Kustantaja | IEEE |
| Sivut | 3913-3921 |
| Sivumäärä | 9 |
| ISBN (elektroninen) | 978-1-7281-5023-9 |
| ISBN (painettu) | 978-1-7281-5024-6 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 2019 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE International Conference on Computer Vision Workshops - Kesto: 1 tammikuuta 1900 → … |
Julkaisusarja
| Nimi | IEEE International Conference on Computer Vision workshops |
|---|---|
| ISSN (painettu) | 2473-9936 |
| ISSN (elektroninen) | 2473-9944 |
Conference
| Conference | IEEE International Conference on Computer Vision Workshops |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
Recent works on no-reference image quality assessment (NR-IQA) have reported good performance for various datasets. However, they suffer from significant performance drops in cross-dataset evaluations which indicates poor generalization power. We propose a Siamese architecture and training procedures for cross-dataset deep NR-IQA that achieves clearly better performance. Moreover, we show that the architecture can be further boosted by i) pre-training with a large aesthetics dataset and ii) adding low-level quality cues, sharpness, tone and colourfulness, as additional features.
Julkaisufoorumi-taso
Tilastokeskuksen tieteenalat
Latausten tilastot
Ei tietoja saatavilla