VALID: Visual quality Assessment for Light field Images Dataset

In the last years, light field imaging has experienced a surge of popularity among the scientific community for its capability of rendering the 3D world in a more immersive way. In particular, several compression algorithms have been proposed to efficiently reduce the amount of data generated in the acquisition process, and different methodologies have been designed to reliably evaluate the visual quality of compressed contents. In this paper we propose a dataset for visual quality assessment of light field images (VALID). The dataset contains five contents compressed at various bitrates, using both off-the-shelf solutions and state-of-the-art algorithms. Results of objective quality evaluation using popular image metrics are included, as well as annotated subjective scores using three different methodologies and two types of visualization setups. The proposed dataset will help develop new objective metrics to predict visual quality, design new subjective assessment methodologies and compare them to existing ones, as well as produce novel analysis approaches to interpret the results.