Using Image Quality Assessment (IQA) Databases to Provide an Appraisal of the Ability of the Feature Selective Validation Method (FSV) to Compare Two-Dimensional Datasets
| Autor: | Alistair Duffy, Antonio Orlandi, Gang Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Image quality 020206 networking & telecommunications 02 engineering and technology Condensed Matter Physics computer.software_genre Atomic and Molecular Physics and Optics Correlation Feature (computer vision) Distortion 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Computational electromagnetics 020201 artificial intelligence & image processing Data mining Electrical and Electronic Engineering computer Image type |
| Zdroj: | IEEE Transactions on Electromagnetic Compatibility. 60:890-898 |
| ISSN: | 1558-187X 0018-9375 |
| DOI: | 10.1109/temc.2017.2771159 |
| Popis: | This paper investigates the strengths and drawbacks of the recently developed feature selective validation (FSV)-2D method. Considering that a subjective benchmark for the validation of two-dimensional computational electromagnetics data is not available, five datasets with subjective scores, commonly used in image quality assessment, are used. It is found that the FSV-2D prediction is influenced by image type and distortion type. Encouraged by the assessment results, eight parameters of the FSV-2D method are optimized by use of genetic algorithms. It is shown that the optimized FSV-2D method provides better correlation with subjective scores. Good agreement with theoretical analysis for computational electromagnetic data further validates the proposed approach. |
| Databáze: | OpenAIRE |
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