Towards a quality metric for dense light fields
This work addresses the need for quality assessment in light-field processing for applications like compression and display, but it is incremental as it primarily benchmarks existing methods on new data.
The authors tackled the problem of quantifying quality loss in dense light fields by collecting a new dataset with perceptual scores and testing existing metrics, finding that while image metrics work well with reference light fields, they perform poorly for comparing distorted ones, highlighting the need for specialized metrics.
Light fields become a popular representation of three dimensional scenes, and there is interest in their processing, resampling, and compression. As those operations often result in loss of quality, there is a need to quantify it. In this work, we collect a new dataset of dense reference and distorted light fields as well as the corresponding quality scores which are scaled in perceptual units. The scores were acquired in a subjective experiment using an interactive light-field viewing setup. The dataset contains typical artifacts that occur in light-field processing chain due to light-field reconstruction, multi-view compression, and limitations of automultiscopic displays. We test a number of existing objective quality metrics to determine how well they can predict the quality of light fields. We find that the existing image quality metrics provide good measures of light-field quality, but require dense reference light- fields for optimal performance. For more complex tasks of comparing two distorted light fields, their performance drops significantly, which reveals the need for new, light-field-specific metrics.