20 citations · 113 across the 20 of their papers we have counts for
5 papers · 1 filter
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network
Saiping Zhang, Luis Herranz, Marta Mrak +3
In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to…
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…
Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture
Issa Khalifeh, Marc Gorriz Blanch, Ebroul Izquierdo +1
Video frame interpolation involves the synthesis of new frames from existing ones. Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this fi…
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…