Decomposition of Multiview Imagery into Diffuse and Specular Components Using Rate-Distortion PROJECT TITLE : Rate-Distortion Driven Decomposition of Multiview Imagery to Diffuse and Specular Components ABSTRACT: We provide a method for compressing multiview photography that utilises an overly full representation. Multiview datasets are decomposed into two additive portions, diffuse and specular content, using a rate-distortion (R-D) driven technique. Decomposition is driven exclusively by compressibility by employing an R-D-inspired measure as our optimization cost function for the diffuse and specular components. For the sake of simplicity, we begin by outlining a framework for doing data separation within a registered domain. Specular data can then be separated from the coordinates of various reference views using a more complete approach. A coding boost of up to 0.6 dB for synthetic datasets and up to 0.9 dB for real datasets has been demonstrated in experiments. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Deep Generative Image Quality Prediction Hankel-Structured Low-Rank Matrix Recovery for Binary Shape Reconstruction from Blurred Images
