With the emergence of high-dynamic range (HDR) imaging, the existing visual signal processing systems will need to deal with both HDR and standard dynamic range (SDR) signals. In such systems, computing the objective quality is an important aspect in various optimization processes (e.g., video encoding). To that end, we present a newly calibrated objective method that can tackle both HDR and SDR signals. As it is based on the previously proposed HDR-VDP-2 method, we refer to the newly calibrated metric as HDR-VDP-2.2. Our main contribution is toward improving the frequency-based pooling in HDR-VDP-2 to enhance its objective quality prediction accuracy. We achieve this by formulating and solving a constrained optimization problem and thereby finding the optimal pooling weights. We also carried out extensive cross-validation as well as verified the performance of the new method on independent databases. These indicate clear improvement in prediction accuracy as compared with the default pooling weights. The source codes for HDR-VDP-2.2 are publicly available online for free down-load and use. 1 Introduction Human eyes have a remarkable ability to adapt and adjust to varying luminance conditions. As a result, humans can clearly visualize and see in lighting conditions ranging from a moonlit night to bright sunshine. In terms of physical lumi-nance values, the former is in the range of about 10 −2 cd∕m 2 , while the latter is more than 10 7 cd∕m 2 , a dynamic range in excess of 9 orders of magnitude. However, when it comes to scene capture and display, such large luminance ranges are beyond the capabilities of current standard dynamic range (SDR) imaging systems. Nevertheless, with the emergence of HDR imaging, it is now possible to capture and display scenes that can encapsulate much a higher dynamic range (HDR) than the traditional or SDR imaging techniques.