FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception. While many other metrics are available, our work provides the first practical treatment of these three central aspects of vision simultaneously. The complex interplay between spatial and temporal sensitivity across retinal locations is especially important for displays that cover a large field-of-view, such as Virtual and Augmented Reality displays, and associated methods, such as foveated rendering. Our metric is derived from psychophysical studies of the early visual system, which model spatio-temporal contrast sensitivity, cortical magnification and contrast masking. It accounts for physical specification of the display (luminance, size, resolution) and viewing distance. To validate the metric, we collected a novel foveated rendering dataset which captures quality degradation due to sampling and reconstruction. To demonstrate our algorithm's generality, we test it on 3 independent foveated video datasets, and on a large image quality dataset, achieving the best performance across all datasets when compared to the state-of-the-art.
b) (a) (d) (c) Figure 1: Our method produces depth-enhanced multiview content from stereo images while preserving the original artistic intent. (a) and (b) show the linearly mapped disparities as well as enhanced disparities computed using our method. (c) and (d)show the result of stereo-to-multiview conversion using (a) and (b), respectively. Our method avoids the cardboarding effect that can be seen in the linearly mapped version. AbstractWe present a novel stereo-to-multiview video conversion method for glasses-free multiview displays. Different from previous stereo-to-multiview approaches, our mapping algorithm utilizes the limited depth range of autostereoscopic displays optimally and strives to preserve the scene's artistic composition and perceived depth even under strong depth compression. We first present an investigation of how perceived image quality relates to spatial frequency and disparity. The outcome of this study is utilized in a two-step mapping algorithm, where we (i) compress the scene depth using a non-linear global function to the depth range of an autostereoscopic display, and (ii) enhance the depth gradients of salient objects to restore the perceived depth and salient scene structure. Finally, an adapted image domain warping algorithm is proposed to generate the multiview output, which enables overall disparity range extension.
The perceived discrepancy between continuous motion as seen in nature and frame-by-frame exhibition on a display, sometimes termed judder, is an integral part of video presentation. Over time, content creators have developed a set of rules and guidelines for maintaining a desirable cinematic look under the restrictions placed by display technology without incurring prohibitive judder. With the advent of novel displays capable of high brightness, contrast, and frame rates, these guidelines are no longer sufficient to present audiences with a uniform viewing experience. In this work, we analyze the main factors for perceptual motion artifacts in digital presentation and gather psychophysical data to generate a model of judder perception. Our model enables applications like matching perceived motion artifacts to a traditionally desirable level and maintain a cinematic motion look.
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