Data Reduction Methods for Deep Images
Abstract: Deep images for use in visual effects work during deep compositing tend to be very large. Quite often the files are larger than needed for their final purpose, which opens up an opportunity for optimizations. This research project is about finding methods for identifying redundant and excessive data use in deep images, and then approximate this data by resampling it and representing it using less data. Focus was on maintaining the final visual quality while optimizing the files so the methods can be used in a sharp production environment. While not being very successful processing geometric data, the results when optimizing volumetric data were very succesfull and over the expectations.
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