研究目的
To develop an effective image quality assessment model for light field images that is consistent with human visual system perception.
研究成果
The proposed MDFM model outperforms state-of-the-art IQA methods in predicting the perceptual quality of light field images, achieving higher accuracy and lower computational time, making it suitable for practical applications.
研究不足
The model is specifically designed for light field images and may not generalize well to other image types. The parameters C1 and C2 are empirically determined, which could be optimized further.
1:Experimental Design and Method Selection:
The model uses discrete derivative filters to extract multi-order derivative features from reference and distorted light field images, measures similarities, and employs a second-order derivative-based pooling strategy.
2:Sample Selection and Data Sources:
The dense light fields database is used, containing synthetic and real-world scenes with various distortions.
3:List of Experimental Equipment and Materials:
A computer with Intel I7-6700 CPU@
4:40 GHz and 16 GB RAM, and MATLAB R2016b software. Experimental Procedures and Operational Workflow:
Extract derivative features, compute similarity maps, generate weight map, pool similarities, and calculate final score.
5:Data Analysis Methods:
Performance evaluated using PLCC, SROCC, KROCC, and RMSE metrics after logistic transformation.
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