20 citations · 60 across the 12 of their papers we have counts for
15 papers · 1 filter
Slimmable Video Codec
Zhaocheng Liu, Luis Herranz, Fei Yang +4
Neural video compression has emerged as a novel paradigm combining trainable multilayer neural networks and machine learning, achieving competitive rate-distortion (RD) performance…
Complexity Reduction of Learned In-Loop Filtering in Video Coding
Woody Bayliss, Luka Murn, Ebroul Izquierdo +2
In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output. Conventional in-loop filters a…
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video
Saiping Zhang, Luis Herranz, Marta Mrak +3
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to…
DVC-P: Deep Video Compression with Perceptual Optimizations
Saiping Zhang, Marta Mrak, Luis Herranz +3
Recent years have witnessed the significant development of learning-based video compression methods, which aim at optimizing objective or perceptual quality and bit rates. In this…
Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding
Luka Murn, Saverio Blasi, Alan F. Smeaton +1
The versatility of recent machine learning approaches makes them ideal for improvement of next generation video compression solutions. Unfortunately, these approaches typically bri…
Towards Transparent Application of Machine Learning in Video Processing
Luka Murn, Marc Gorriz Blanch, Maria Santamaria +2
Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning. The new techniques, considered…