Subjective Visual Quality Assessment of Compressed Light Field Images: Learning-based vs. Conventional Methods

Light fields (LF) technology enables the capture and reproduction of a 3D scene accurately, which enhances visual experience in various applications. The sheer volume of multiplicity of the captured views creates logistical problems in both storage and data transmission, which makes LF compression crucial. Even though many LF compression techniques have been proposed and evaluated in recent years, the leading-edge learning-based approaches have not been subject to the same level of scrutiny. This paper presents a subjective quality assessment study on four different LF compression methods, including two learning-based LF compression methods, which have not been studied before from a subjective quality point of view. For this purpose, subjective opinion scores were collected from viewers in two different universities for a cross-lab study. The results indicate that the learning-based compression methods have different behavior in their rate-distortion curves, and that there is room for improvement for learning-based methods. A qualitative analysis also shows that their artifact structures are different from conventional ones. The results highlight the need for a perceptual objective quality metric that takes different types of artifacts into account. The obtained subjective quality database (MiX-LFQDB) is made public to support further research in this area: https://doi.org/10.5281/zenodo.16778670