"One model is based on an analysis of the rendering quality of image-based rendering (IBR), and uses Taylor series expansion to derive an upper bound of the mean absolute error (MAE) of the view synthesis.
"An autoregressive model estimates the synthesis distortion at the block level and is effective for rate-distortion optimized mode selection. A distortion model as a function of the position of the viewpoint is also known for bit allocation."
In addition to obtaining background information on this patent application, VerticalNews editors also obtained the inventors' summary information for this patent application: "The embodiments of the invention provide an analytical model and method for estimating a rendering quality, in virtual images for virtual viewpoints, in a 3D video (3DV). The model relates errors to the rendering quality, taking into account texture image characteristics, texture image quality, camera configuration, i.e., real viewpoints, and the rendering process.
"Specifically, we derive position errors from depth errors, and a probability distribution of the position errors is used to determine a power spectral density (PSD) of the rendering errors.
"The model can accurately estimate synthesis noise up to a constant offset from the real viewpoints. Thus, the model can be used to evaluate a change in rendering quality for systems and methods of different designs.
"We analyze how depth errors relate to the rendering quality, taking into account texture image characteristics, texture image quality, camera configuration and the rendering process. In particular, depth errors are used to determine the position errors, and the probability distribution of the position errors is in turn used to estimate the synthesis noise power at the image level.
"We use the power spectral density (PSD) to analyze the impact of depth errors, in terms of mean square errors (MSE). This relates to prior art work, which used the PSD only to analyze the effect of motion vector inaccuracy, and disparity inaccuracy.
"However, while previous work applied PSD to analyze the efficiency of the motion and disparity compensated predictors in predictive coding, we use the PSD to quantify the noise power in virtual images produced by a rendering pipeline.
"Although we focus on texture and depth errors due to predictive coding, we make no assumption on how information was distorted to produce the errors. We focus on the transformation and interaction of the texture and depth errors in the synthesis pipeline.
BRIEF DESCRIPTION OF THE DRAWINGS
"FIG. 1 is a schematic of processing in a view synthesis pipeline, according to embodiment of the invention;
"FIGS. 2A-2C are schematics of acquired texture and acquired depth images, reconstructed texture and acquired depth images, and reconstructed texture and reconstructed depth images, respectively;
"FIG. 3 are graphs of an empirical distribution probability density function of a position error and a frequency envelope after a fast Fourier transform; and
"FIG. 4 is a flow diagram of a method for determining a quality of a virtual image according to embodiments of the invention."
For more information, see this patent application: Cheung, Ngai-Man; Tian, Dong; Vetro, Anthony; Sun, Huifang. Method for Modeling and Estimating Rendering Errors in Virtual Images. U.S. Patent Application Serial Number 366348, filed
Keywords for this news article include: Patents.
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