Most of the snapshot HDR (High Dynamic Range) image sensors have a non-linear, programmable, response curve that requires multiple register settings. The complexity of the settings is such that most algorithms reduce the number of parameters to only two or three and calculate a smooth response curve that approaches a log response. The information available in the final image depends on the compression rate of the response curve and the quantization step of the device. In this early stage proposal, we make use of scene information and discrete information transfer to calculate the response curve shape that maximizes the information in the final image. The image may look different to a human but contains more useful information for machine vision processing. One important field of use of such sensors with programmable dynamic range is automotive on-board machine vision and more specifically autonomous vehicles.
Automatic generation of data visualizations allows to quickly deploy data visualizations. In visual analytics, the combination of automatic and human analysis increases the effort necessary to achieve similar effects substantially. Where automatic visualization only needs to map the data, in visual analytics the whole data preparation and processing pipeline has to be considered. The user is interested in representations reflecting certain interpretations of the data, for example the idea that different groups represent different clusters in the data. In this paper, we prove that an information-driven automatic design of visual analytics pipelines is feasible. To this end, we prove that the ability of an analysis system to derive and visualize data supporting inquired information is decidable – at least for real-world applications. Having overcome this major obstacle, we outline a general algorithm scheme that can be implemented on a wide range of data and information models.