A single tone curve that remaps the brightnesses of each image pixel is a simple and widely deployed way to enhance an image. Tone curves might be crafted by individual users or determined algorithmically in camera processing pipelines. The precise shape of the tone curve is not a priori strongly constrained, other than it is usually limited to increasing functions of brightness. In this paper, we constrain the shape further and define a tone curve to be simple if it has no or one inflexion point. With respect to our representation, wiggly tone curves have several inflexion points and are deemed to be complex. A key contribution of our work is to show how we can best approximate a complex curve with a simple counterpart. For the MIT-Adobe FiveK dataset, comprising thousands of images that are tone-adjusted by photographic experts, we calculate corresponding simple tone curve adjusted images. Using objective similarity metrics, we find that simple curves deliver equally good image enhancement. In terms of preference experiments, simple curves deliver slightly preferred images compared to complex counterparts. Similar results are reported for a second smaller underwater image dataset.

Large Language Models (LLMs) have the potential to transform humanities and social science research, yet their history knowledge and comprehension at a graduate level remains untested. Benchmarking LLMs in history is particularly challenging, given that human knowledge of history is inherently unbalanced, with more information available on Western history and recent periods. We introduce the History Seshat Test for LLMs (HiST-LLM), based on a subset of the Seshat Global History Databank, which provides a structured representation of human historical knowledge, containing 36,000 data points across 600 historical societies and over 2,700 scholarly references. This dataset covers every major world region from the Neolithic period to the Industrial Revolution and includes information reviewed and assembled by history experts and graduate research assistants. Using this dataset, we benchmark a total of seven models from the Gemini, OpenAI, and Llama families. We find that, in a four-choice format, LLMs have a balanced accuracy ranging from 33.6% (Llama-3.1-8B) to 46% (GPT-4-Turbo), outperforming random guessing (25%) but falling short of expert comprehension. LLMs perform better on earlier historical periods. Regionally, performance is more even but still better for the Americas and lowest in Oceania and Sub-Saharan Africa for the more advanced models. Our benchmark shows that while LLMs possess some expert-level historical knowledge, there is considerable room for improvement.