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Found 10 papers, 5 papers with code
GeoScaler: Geometry and Rendering-Aware Downsampling of 3D Mesh Textures
Towards filling these gaps, we introduce GeoScaler, which is a method of downsampling texture maps of 3D meshes while incorporating geometric cues, and by maximizing the visual fidelity of the rendered views of the textured meshes.
Telepresence Video Quality Assessment
Video conferencing, which includes both video and audio content, has contributed to dramatic increases in Internet traffic, as the COVID-19 pandemic forced millions of people to work and learn from home.
Pik-Fix: Restoring and Colorizing Old Photos
Our proposed framework consists of three modules: a restoration sub-network that conducts restoration from degradations, a similarity network that performs color histogram matching and color transfer, and a colorization subnet that learns to predict the chroma elements of images conditioned on chromatic reference signals.
MaxViT: Multi-Axis Vision Transformer
We also show that our proposed model expresses strong generative modeling capability on ImageNet, demonstrating the superior potential of MaxViT blocks as a universal vision module.
Ranked #1 on
Object Detection
on COCO 2017
MAXIM: Multi-Axis MLP for Image Processing
In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks.
Ranked #1 on
Deblurring
on HIDE (trained on GOPRO)
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.
Patch-VQ: 'Patching Up' the Video Quality Problem
No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem to social and streaming media applications.
Ranked #10 on
Video Quality Assessment
on LIVE-FB LSVQ
(using extra training data)
180-degree Outpainting from a Single Image
Two networks are trained separately, and then are employed jointly to sequentially perform narrow-to-90{\deg} generation and 90{\deg}-to-180{\deg} generation.
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts billions of viewers daily.
Ranked #4 on
Video Quality Assessment
on MSU SR-QA Dataset
