Currently available visual noise estimation algorithms are primarily developed and calibrated using SDR images, which limits their accuracy in representing the actual noise perceived by humans in HDR content. One key factor often overlooked is the luminance adaptation of the observer, especially when there is a significant contrast between the observed patch and its surrounding area. Moreover, the design of existing test charts, combined with increasingly sophisticated local tone mapping algorithms, introduces new challenges. A prominent issue is the presence of gradients in the final image, which significantly affect algorithmic measurements but have minimal impact on human perception: for instance, a patch may register a Just Noticeable Difference (JND) of 6 compared to a perfectly clean patch with zero noise, despite being visually clean and not visibly different. This paper proposes a new direction for visual noise algorithms and sets the foundations for future research. It presents findings on: 1) A new HDR ruler for visual noise assessment. 2) The impact of various factors (CSF, HPF, gradient correction) on algorithm performance. 3) Evaluation of different color spaces to calculate visual noise metrics.