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Found 12 papers, 2 papers with code
Perceptually-inspired super-resolution of compressed videos
Spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency.
A Subjective Study on Videos at Various Bit Depths
Bit depth adaptation, where the bit depth of a video sequence is reduced before transmission and up-sampled during display, can potentially reduce data rates with limited impact on perceptual quality.
CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement
We propose a new Generative Adversarial Network for Compressed Video quality Enhancement (CVEGAN).
Video Compression with CNN-based Post Processing
In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content.
Video compression with low complexity CNN-based spatial resolution adaptation
It has recently been demonstrated that spatial resolution adaptation can be integrated within video compression to improve overall coding performance by spatially down-sampling before encoding and super-resolving at the decoder.
MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering
Each MFRB extracts features from multiple convolutional layers using dense connections and a multi-level residual learning structure.
BVI-DVC: A Training Database for Deep Video Compression
Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches.
Understanding the Political Ideology of Legislators from Social Media Images
In this paper, we seek to understand how politicians use images to express ideological rhetoric through Facebook images posted by members of the U. S. House and Senate.
Generating Image Sequence from Description with LSTM Conditional GAN
Generating images from word descriptions is a challenging task.
Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask
We show that by using masks the motion estimate results in a quadratic function of input features in the output layer.
Lecture video indexing using boosted margin maximizing neural networks
This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system.
CGMOS: Certainty Guided Minority OverSampling
In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance.
