Cross Data Set Performance Consistency of Objective Quality Assessment Methods for Light Fields

With the emergence of light field (LF) imaging technology, various challenging problems for LF visualization, compression, and transmission have to be addressed. Measuring perceptual quality is of utmost importance to assess the impact of an LF processing step on the visual experience of the finally rendered content to the end-user. In particular, objective quality assessment (QA) plays a key role in the quality optimization of LF imaging systems. In this paper, we conducted a comprehensive experiment to evaluate the performance of different objective QA methods for LF application. To this end, we selected a total number of 250 LFs (more than 48000 perspective views) from three public data sets to evaluate 16 objective QA metrics. Moreover, the subjective scores from three test data sets were aligned to produce an integrated data set for quality evaluation. The performance results across the different data sets aid to choose the most reliable metrics that are consistently performing well under various distortion and content characteristic conditions.