Spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency.
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.
In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content.
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.
Each MFRB extracts features from multiple convolutional layers using dense connections and a multi-level residual learning structure.
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.
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.
We show that by using masks the motion estimate results in a quadratic function of input features in the output layer.
In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance.