Adapting Quality Metrics to Tone Mapping

Abstract

Tone mapping evaluation is difficult because of the substantial differences in absolute luminance between high dynamic range (HDR) reference and tone-mapped standard dynamic range (SDR) test content. To address this challenge, we collected a new tone mapping evaluation dataset, focused on fundamental tone mapping operations, and combined it with several existing tone mapping quality assessment datasets. Rather than introducing new specialized metrics designed for tone-mapped content, we instead developed a set of techniques to adapt existing quality metrics for tone mapping quality assessment. Our approach models the photometric differences between HDR reference and SDR test displays for accurate metric predictions. The technique consists of two steps: first, a display model converts display-encoded content to photometric values; second, these values are re-encoded using a perceptual transfer function to map both HDR and tone-mapped images to the same display-encoded color space. We systematically evaluated both general-purpose image and video quality metrics with our adaptations and those specifically designed for tone mapping. With these adjustments, general-purpose metrics perform much better for tone mapping evaluation, consistently outperforming previously established specialized techniques. Additionally, we adapted the ColorVideoVDP metric to be sensitive to absolute luminance changes, resulting in \textit{\ourmethod}, which shows greatly improved performance and accepts photometric values as input. These results highlight the robustness of our adaptation technique and provide an improved protocol to evaluate future tone mapping quality metrics. Our datasets, code, and supplementary results can be found at kenchen10.github.io/projects/tmometric/index.html.