No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features
During acquisition, storage, and transmission, the quality of digital videos degrades significantly. Low-quality videos lead to the failure of many computer vision applications, such as object tracking or detection, intelligent surveillance, etc. Over the years, many different features have been dev...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/24/9696 |
| _version_ | 1797455371057496064 |
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| author | Domonkos Varga |
| author_facet | Domonkos Varga |
| author_sort | Domonkos Varga |
| collection | DOAJ |
| description | During acquisition, storage, and transmission, the quality of digital videos degrades significantly. Low-quality videos lead to the failure of many computer vision applications, such as object tracking or detection, intelligent surveillance, etc. Over the years, many different features have been developed to resolve the problem of no-reference video quality assessment (NR-VQA). In this paper, we propose a novel NR-VQA algorithm that integrates the fusion of temporal statistics of local and global image features with an ensemble learning framework in a single architecture. Namely, the temporal statistics of global features reflect all parts of the video frames, while the temporal statistics of local features reflect the details. Specifically, we apply a broad spectrum of statistics of local and global features to characterize the variety of possible video distortions. In order to study the effectiveness of the method introduced in this paper, we conducted experiments on two large benchmark databases, i.e., KoNViD-1k and LIVE VQC, which contain authentic distortions, and we compared it to 14 other well-known NR-VQA algorithms. The experimental results show that the proposed method is able to achieve greatly improved results on the considered benchmark datasets. Namely, the proposed method exhibits significant progress in performance over other recent NR-VQA approaches. |
| first_indexed | 2024-03-09T15:52:28Z |
| format | Article |
| id | doaj.art-4f946f7c15354dd59c34a42c1cb01622 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:28Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4f946f7c15354dd59c34a42c1cb016222023-11-24T17:53:49ZengMDPI AGSensors1424-82202022-12-012224969610.3390/s22249696No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image FeaturesDomonkos Varga0Ronin Institute, Montclair, NJ 07043, USADuring acquisition, storage, and transmission, the quality of digital videos degrades significantly. Low-quality videos lead to the failure of many computer vision applications, such as object tracking or detection, intelligent surveillance, etc. Over the years, many different features have been developed to resolve the problem of no-reference video quality assessment (NR-VQA). In this paper, we propose a novel NR-VQA algorithm that integrates the fusion of temporal statistics of local and global image features with an ensemble learning framework in a single architecture. Namely, the temporal statistics of global features reflect all parts of the video frames, while the temporal statistics of local features reflect the details. Specifically, we apply a broad spectrum of statistics of local and global features to characterize the variety of possible video distortions. In order to study the effectiveness of the method introduced in this paper, we conducted experiments on two large benchmark databases, i.e., KoNViD-1k and LIVE VQC, which contain authentic distortions, and we compared it to 14 other well-known NR-VQA algorithms. The experimental results show that the proposed method is able to achieve greatly improved results on the considered benchmark datasets. Namely, the proposed method exhibits significant progress in performance over other recent NR-VQA approaches.https://www.mdpi.com/1424-8220/22/24/9696no-reference video quality assessmentquality-aware featuresmulti-feature fusion |
| spellingShingle | Domonkos Varga No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features Sensors no-reference video quality assessment quality-aware features multi-feature fusion |
| title | No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features |
| title_full | No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features |
| title_fullStr | No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features |
| title_full_unstemmed | No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features |
| title_short | No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features |
| title_sort | no reference video quality assessment using the temporal statistics of global and local image features |
| topic | no-reference video quality assessment quality-aware features multi-feature fusion |
| url | https://www.mdpi.com/1424-8220/22/24/9696 |
| work_keys_str_mv | AT domonkosvarga noreferencevideoqualityassessmentusingthetemporalstatisticsofglobalandlocalimagefeatures |