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Found 5 papers, 2 papers with code
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network
In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos.
Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture
Despite all the benefits interpolation methods offer, many of these networks require a lot of parameters, with more parameters meaning a heavier computational burden.
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video
Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos.
Towards Transparent Application of Machine Learning in Video Processing
Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning.
Attention-based Stylisation for Exemplar Image Colourisation
Stylised outputs are then obtained by computing similarities between both feature representations in order to transfer the style of the reference to the content of the target input.
