No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features
The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions du...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/7/7/112 |
| _version_ | 1797526816213172224 |
|---|---|
| author | Domonkos Varga |
| author_facet | Domonkos Varga |
| author_sort | Domonkos Varga |
| collection | DOAJ |
| description | The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ). |
| first_indexed | 2024-03-10T09:35:40Z |
| format | Article |
| id | doaj.art-c22c95b1dc694ca48368d9fcc17f6dc3 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T09:35:40Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-c22c95b1dc694ca48368d9fcc17f6dc32023-11-22T04:08:39ZengMDPI AGJournal of Imaging2313-433X2021-07-017711210.3390/jimaging7070112No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep FeaturesDomonkos Varga0Independent Researcher, H-1139 Budapest, HungaryThe goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).https://www.mdpi.com/2313-433X/7/7/112no-reference image quality assessmentdeep learningconvolutional neural networks |
| spellingShingle | Domonkos Varga No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features Journal of Imaging no-reference image quality assessment deep learning convolutional neural networks |
| title | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
| title_full | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
| title_fullStr | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
| title_full_unstemmed | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
| title_short | No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features |
| title_sort | no reference image quality assessment with multi scale orderless pooling of deep features |
| topic | no-reference image quality assessment deep learning convolutional neural networks |
| url | https://www.mdpi.com/2313-433X/7/7/112 |
| work_keys_str_mv | AT domonkosvarga noreferenceimagequalityassessmentwithmultiscaleorderlesspoolingofdeepfeatures |