A Robust Large-scale Dataset for Assessing Light Field Image Quality

In recent years, light field imaging has gained significant attention in the scientific community due to its ability to provide a more immersive representation of the 3D world. However, ensuring the quality of light field images is crucial for their subsequent processing and applications. Deep learning methods, leveraging neural networks, have shown promising performance in Image Quality Assessment (IQA). However, the unique characteristics of light field data pose a challenge for existing IQA methods. To address this challenge, we propose a Robust Large-scale Dataset for Assessing Light Field Image Quality, named RLSD, specifically designed for evaluating the quality of light field images. The dataset comprises both real and synthetic scenes, covering a wide range of key low attributes and including three representative distortions: compression, noise, and blur. To obtain subjective evaluations, we adopt the single stimulus continuous quality evaluation (SSCQE) method and compute the Mean Opinion Score (MOS). We performed statistical analysis on the dataset and experimental results indicate that our proposed RLSD dataset includes various common scenes and distortion levels, making it suitable for designing and evaluating LF-IQA algorithms. The dataset is publicly available at the following link: “https://pan.baidu.com/s/1kJmx4qsy8ywLPba-HwGCEg” (password: XY28).