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Found 8 papers, 3 papers with code
Online Overexposed Pixels Hallucination in Videos with Adaptive Reference Frame Selection
We present a learning-based system to reduce these artifacts without resorting to complex acquisition mechanisms like alternating exposures or costly processing that are typical of high dynamic range (HDR) imaging.
FOVQA: Blind Foveated Video Quality Assessment
Previous blind or No Reference (NR) video quality assessment (VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain.
Evaluating Foveated Video Quality Using Entropic Differencing
To reduce stresses on bandwidth, foveated video compression is regaining popularity, whereby the space-variant spatial resolution of the retina is exploited.
FovVideoVDP: A visible difference predictor for wide field-of-view video
FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception.
Ranked #20 on
Video Quality Assessment
on MSU FR VQA Database
Learning to Compress Videos without Computing Motion
Our framework exploits the regularities inherent to video motion, which we capture by using displaced frame differences as video representations to train the neural network.
Semi-Supervised StyleGAN for Disentanglement Learning
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation.
Disentangled GANs for Controllable Generation of High-Resolution Images
Generative adversarial networks (GANs) have achieved great success at generating realistic samples.
Spoke-Darts for High-Dimensional Blue-Noise Sampling
Blue noise sampling has proved useful for many graphics applications, but remains underexplored in high-dimensional spaces due to the difficulty of generating distributions and proving properties about them.
Graphics
