Objective and Subjective Evaluation of Virtual Relighting from Reflectance Transformation Imaging Data

Abstract

Reflectance Transformation Imaging (RTI) is widely used to produce relightable models from multi-light image collections.These models are used for a variety of tasks in the Cultural Heritage field. In this work, we carry out an objective and sub-jective evaluation of RTI data visualization. We start from the acquisition of a series of objects with different geometry andappearance characteristics using a common dome-based configuration. We then transform the acquired data into relightablerepresentations using different approaches: PTM, HSH, and RBF. We then perform an objective error estimation by comparingground truth images with relighted ones in a leave-one-out framework using PSNR and SSIM error metrics. Moreover, we carryout a subjective investigation through perceptual experiments involving end users with a variety of backgrounds. Objective andsubjective tests are shown to behave consistently, and significant differences are found between the various methods. While theproposed analysis has been performed on three common and state-of-the-art RTI visualization methods, our approach is gen-eral enough to be extended and applied in the future to new developed multi-light processing pipelines and rendering solutions,to assess their numerical precision and accuracy, and their perceptual visual quality.