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Improved Image Quality Assessment by Utilizing Pre-Trained Architecture Features with Unified Learning Mechanism.
- Source :
- Applied Sciences (2076-3417); Feb2023, Vol. 13 Issue 4, p2682, 10p
- Publication Year :
- 2023
-
Abstract
- The purpose of the no-reference image quality assessment (NR-IQA) is to measure perceived image quality based on subjective judgments; however, due to the lack of a clean reference image, this is a complicated and unresolved challenge. Massive new IQA datasets have facilitated the creation of deep learning-based image quality measurements. We present a unique model to handle the NR-IQA challenge in this research by employing a hybrid strategy that leverages from pre-trained CNN model and the unified learning mechanism that extracts both local and non-local characteristics from the input patch. The deep analysis of the proposed framework shows that the model uses features and a mechanism that improves the monotonicity relationship between objective and subjective ratings. The intermediary goal was mapped to a quality score using a regression architecture. To extract various feature maps, a deep architecture with an adaptive receptive field was used. Analyses of this biggest NR-IQA benchmark datasets demonstrate that the suggested technique outperforms current state-of-the-art NR-IQA measures. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
PERCEIVED quality
LEARNING
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 13
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 162083536
- Full Text :
- https://doi.org/10.3390/app13042682